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Hand, Brain and Technology, CSF Conference, Monte Verità, September 7-12, 2014 0

KEYNOTE SPEAKERS

Francisco Valero-Cuevas

Susan Goldin-Meadow

Hansjörg Scherberger

Joachim Hermsdörfer

Georg Goldenberg

Nicole Wenderoth

Giuseppe Luppino

Andrew Schwartz

Joern Diedrichsen

Roland Johansson

Sliman Bensmaia

Derek Kamper

Andrea Serino

Tamar Makin

Frank Wilson

Marc Schieber

Vincent Hayward

Marco Santello

Roger Lemon

Angela Sirigu

Todd Kuiken

Eric Rouiller

Lee Miller

ORGANIZERS

Roger Gassert, Peter Brugger,

Marie-Claude Hepp-Reymond,

Fabio M. Conti, Mike D. Rinderknecht

HAND, BRAIN &

TECHNOLOGY

Monte Verità, Ascona, Switzerland, September 7 – 12, 2014


WELCOME Dear Attendees, A warm welcome to the “Hand, Brain and Technology” Conference on Monte Verità, held within the Congressi Stefano Franscini conference series! The tight functional coupling between hand and brain has greatly shaped the evolution of language, culture and technology. Any reduction or loss of hand function, whether of central or peripheral origin, has devastating effects on the independence and social integration of the affected person. And any treatment, be it through human or technological intervention, must account for this unique coupling. As a result, hand and brain have drawn strong interest from the social, medical and engineering sciences alike. This conference brings together leading researchers from the multiple disciplines studying the unique dexterity and sensory abilities of the hand, its neuromechanical and physiological control, as well as its functional recovery and neuroprosthetic restoration following injury.

This is the third CSF conference on these topics, following two successful events organized by Mario Wiesendanger in 1994 (Sensorimotor Function of the Hand: Mechanics and Control) and 1998 in collaboration with Marie-Claude Hepp-Reymond (Neural Basis of Hand Dexterity), the former of which resulted in a book entitled “Hand and Brain – The Neurophysiology and Psychology of Hand Movements”, which has become a seminal work in the field. This conference adds to the previous two by integrating an engineering component, with the aim of promoting interaction and collaboration across scientific disciplines.

Aligned with the philosophy of CSF conferences, there will be ample time for discussions, as well as for junior and advanced researchers to disseminate their work and interact with senior researchers. We thank you all for coming, many of you over great distances, for contributing to this unique event and for your time. We also thank our sponsors and the CSF for making this event possible, and wish you an intellectually stimulating and inspiring week on Monte Verità! Sincerely,

Prof. Roger Gassert Rehabilitation engineering

Department of Health Sciences and Technology, ETH Zurich

Prof. Peter Brugger Neuropsychology

Department of Neurology University Hospital Zurich

Prof. Marie-Claude Hepp-Reymond Neural control of grasping

Institute of Neuroinformatics ETH Zurich and University of Zurich

Dr. med. Fabio Mario Conti Neurocognitive rehabilitation

Clinica Hildebrand Centro di riabilitazione Brissago

Mike Domenik Rinderknecht Rehabilitation engineering

Department of Health Sciences and Technology, ETH Zurich


SPONSORS


VENUE The CSF (Congressi Stefano Franscini) conference Hand, Brain and Technology 2014 is held on the Monte Verità in Ascona, Switzerland.


TRANSPORTATION There will be a FREE SHUTTLE SERVICE (from Locarno train station to Monte Verità). We are offering a limited number of seats (max 11 people every 60 min) in a shuttle from the train station in Locarno to the conference venue on Monte Verità on the arrival and departure day. This shuttle is based on a first-come, first-served basis, with the following departure timetable:

Sunday, September 7, 2014 (Departure from Locarno)

Friday, September 15, 2014 (Departure from Monte Verità)

15:20 13:20

16:20 14:20

17:20 15:20

18:20 16:20

19:20 17:20

20:20 18:20

For public transportation throughout Switzerland consult www.sbb.ch/en.

WIRELESS LAN You will find access information for the Wi-Fi in the conference bag. Additionally to the wireless LAN, a computer room at the conference venue is available for the participants.

EXCURSION & CONFERENCE DINNER As one of the highlights of the "Hand, Brain and Technology" conference, we invite you to join us for a scenic excursion to discover the beautiful countryside of our region on the afternoon of Wednesday, September 10th. The program includes a journey by boat on Lago Maggiore departing from Locarno, followed by a lakeside dinner in the Camin Hotel in Colmegna, a historic villa set in an ancient park stretching along the Verbano bank on the Italian side of the lake. Sponsored by the Clinica Hildebrand Centro di riabilitazione Brissago and the Ente Ospedaliero Cantonale, this excursion promises to be a wonderful opportunity to engage with your fellow attendees around authentic Italian cuisine amongst unforgettable surroundings.

http://www.sbb.ch/en


SPECIAL LECTURE BY FRANK WILSON

KEYNOTE

Hand, Brain, and Self: What becomes of Homo sapiens in the age of the "smart" machine?

F. Wilson1*

1 Stanford University, Professor Emeritus

Abstract In 1511, when Michelangelo painted the hand of God reaching toward the hand of Adam on the ceiling

of the Sistine Chapel, he gave the Renaissance not just its single most iconic image but its most

potent spiritual and psychological message: the human hand is an instrument whose highest use

requires more than mere physical control. Michelangelo's message has resonated deeply with both

religious and secular thinkers for over 500 years, but this may not be the time to discard it. Indeed, it

is Dr. Wilson’s thesis that this message has acquired renewed importance by challenging us to think

critically (and humanly) about the science and technology that are revolutionizing our understanding of

ourselves, our lives, and our future.

Short Biography Frank Wilson is an American neurologist whose decades of clinical work and research involving artists

with hand problems have been influential in bringing researchers in anthropology, evolutionary

biology, and neuroscience into closer theoretical and practical contact. Now retired from active

practice, he was Visiting Professor of Neurology at the University of Düsseldorf; medical director of the

Health Program for Performing Artists at the University of California, San Francisco; and Clinical

Professor of Neurology at Stanford University School of Medicine; and is the author of The Hand: How

Its Use Shapes the Brain, Language, and Human Culture. He was awarded an Honorary Doctorate in

Fine Arts by the Massachusetts College of Art and Design in Boston in 2012.

SOCIAL EVENT – DIMITRI & COMPAGNIA DUE Dimitri was born in Ascona and decided to become a clown at the age of seven. While becoming an apprentice potter, he took theatre classes, studied music, ballet and acrobatics. He studied mime with Etienne Decroux, became a member of Marcel Marceau's troupe and appeared as "Auguste" with the famous white clown Maïss at Circus Medrano in Paris. In 1959 he appeared for the first time in a programme of his own in Ascona and soon followed tours throughout the world and with Circus Knie. In 1971, together with his wife Gunda, he founded the Teatro Dimitri in Verscio, in 1975 the Scuola Teatro Dimitri and in 1978 the Compagnia Teatro Dimitri. In the year 2000 Dimitri founded together with Harald Szeemann the Museo Comico in Verscio. Dimitri is still considered one of the world's best clowns who not only makes his public laugh but with his poetic mind and generous heart also deeply touches his audience. Dimitri will give a speech “in plain clothes” about “The hand and the brain”. The group Compagnia DUE presents dexterity numbers, magic, dance, and live acrobatics, all in a non-verbal manner and therefore very understandable to all guests. Andreas Manz and Bernard Stöckli from Compagnia DUE have been in the humor business for many years, with a blatant theatricality and slapstick comedy as trademark, spiced with poetry, clowning, improvisation and a captivating humor. The two professional artists successfully blend the comic entertainment with the demanding art of the theater, mastering both fields with great skill. The show will comprise a variety of comic numbers centered around the theme “The hand and the brain”. More information about Dimitri and Compagnia Due can be found under www.clowndimitri.ch and www.compagniadue.com.


PROGRAM


CORTICAL CONTROL Monday, September 8, 2014

09h00 – 09h20 Welcome address

09h00 – 10h00 Roger Lemon (U College London) KEYNOTE Corticospinal systems for movement generation and action observation

10h00 – 10h20 Thomas Brochier (CNRS Marseille) A13T Mapping horizontal cortical connections in primate motor cortex using intracortical micro-stimulation

10h20 – 10h50 Coffe

10h50 – 11h20 Guiseppe Luppino (U Parma) KEYNOTE Cortical circuits for purposeful hand actions

11h20 – 12h00 Marc Schieber (U Rochester) KEYNOTE Spatiotemporal distribution of object versus location in kinematics, EMG and motor cortex activity during reach, grasp and manipulation

12h00 – 12h20 Rijk Intveld (German Primate Center) A07T Grasp force coding in F5 and AIP in a delayed grasping task

12h30 – 14h30 Lunch

14h30 – 14h50 Poster fast-forward

14h50 – 15h30 Marco Santello (Arizona State U) KEYNOTE Sensorimotor mechanisms for control and learning of dexterous manipulation

15h30 – 15h50 Marco Davare (U College London) A05T Effect of visuo-haptic conflicts on grasping movements in virtual reality

15h50 – 16h20 Coffe


16h40 – 17h20 Jörn Diedrichsen (U College London) KEYNOTE The cortical representation of hand movements

17h20 – 17h40 Anne-Dominique Gindrat (U Fribourg) A10T Effect of primary motor cortex lesion on cortical processing of tactile finger stimulation in adult monkeys: an EEG study

17h40 – 18h00 Miriam Schrafl-Altermatt (Balgrist University Hospital) A15T Neural Coupling of Cooperative Hand Movements

18h30 – 20h30 Dinner

20h30 – Poster session (Sala Balint)


COGNITIVE & CLINICAL

NEUROSCIENCE Tuesday, September 9, 2014

09h00 – 09h40 Susan Goldin-Meadow (U Chicago) KEYNOTE From action to abstraction: Gesture as a mechanism of change

09h40 – 10h20 Georg Goldenberg (Bogenhausen Hospital Munich, TU Munich) KEYNOTE Apraxia tears apart the neural substrates of instrumental and communicative functions of the hand

10h20 – 10h50 Coffee

10h50 – 11h20 Nicole Wenderoth (ETH Zurich, KU Leuven) KEYNOTE Making and breaking motor memories

11h20 – 11h40 Noëmi Eggenberger (University Hospital Bern) B04T Visual exploration of co-speech hand gestures in aphasic patients: An eye-tracking study

11h40 – 12h00 Silvio Ionta (ETH Zurich, CHUV) B01T Neuroplastic sensorimotor adaptations after spinal cord injury

12h00 – 12h20 Zdravko Radman (Zagreb, U Split) B06T The “enhanded” mind: An attempt for a reconception of agency

12h30 – 14h30 Lunch

14h30 – 15h10 Angela Sirigu (CNRS Lyon) KEYNOTE Varieties of movement representations in the human brain

15h10 – 15h50 Andrea Serino (EPFL, U Bologna) KEYNOTE Neural mechanisms, functions and plasticity of peripersonal space representation in humans

15h50 – 16h20 Coffe

16h20 – 17h00 Tamar Makin (U Oxford) KEYNOTE Bridging the gap between cortical reorganisation and rehabilitation in arm amputees

17h00 – 17h20 Elena Rusconi (U Parma) B12T Neural correlates of finger gnosis

17h20 – 17h40 Stefan Vogt (U Lancaster, U Liverpool) B03T Imitation learning of spatial sequences and rhythms: A FMRI study in musically naïves and drummers

17h40 – 18h00 Jennifer Gurd (U Oxford) B07T Hand, brain, attention

18h00 – 18h20 General discussion


20h30 – 21h30 Frank Wilson (Stanford U, Professor Emeritus) KEYNOTE "The Hand: How its use shapes the brain, language, and human culture"


NEUROPROSTHETICS Wednesday, September 10, 2014

09h00 – 09h40 Andrew Schwarz (U Pittsburgh) KEYNOTE Recent progress toward a high-performance neural prosthesis

09h40 – 10h20 Sliman Bensmaia (U Chicago) KEYNOTE Biological and bionic hands: Natural neural coding and artificial perception


10h50 – 11h20 Lee Miller (Northwestern U) KEYNOTE Restoring hand function with a biomimetic neural interface and Functional Electrical Stimulation

11h20 – 12h00 Hansjörg Scherberger (German Primate Center) KEYNOTE Grasp predictions from motor, premotor, and parietal population signals

12h00 – 12h40 Todd Kuiken (RIC, Northwestern U) KEYNOTE Developing neural interfaces for powered prosthetic limbs

13h00 – 15h00 Lunch

15h00 – 21h30 Excursion & conference dinner


HAPTICS & DEXTERITY Thursday, September 11, 2014

09h00 – 09h40 Roland Johansson (Umeå U) KEYNOTE Edge-orientation processing in first-order tactile neurons

09h40 – 10h00 Michael Dimitriou (Umeå U) D04T Human muscle spindles preferentially encode imposed movement

10h00 – 10h20 Ian Bullock (Yale U) D11T Kinematics of two- and three-fingered dexterous precision manipulation


10h50 – 11h20 Eric Rouiller (U Fribourg) KEYNOTE Behavioral variability of manual dexterity in macaques

11h20 – 12h20 Poster session (Sala Balint)

12h30 – 14h30 Lunch

14h30 – 15h10 Francisco Valero-Cuevas (U Southern California) KEYNOTE Moving beyond a cortico-centric view of dexterity

15h10 – 15h50 Aaron Dollar & Thomas Feix (Yale U) D10T Modeling of precision grip in primates

15h50 – 16h20 Coffe

16h20 – 17h00 Vincent Hayward (UPMC Paris) KEYNOTE Mechanics of the fingertip and its impact on the prehensile and sensory function of the hand

17h00 – 17h20 Sarah Wohlman (Northwestern U, RIC) D06T Subject variability during maximum lateral pinch

17h20 – 17h40 Andreas Thomik (Imperial College London) D07T Symbolic representation of complex action sequences



20h30 – 21h30 Social Event: Dimitri & Compagnia Due (Sala Balint)


NEURO-

REHABILITATION Friday, September 12, 2014

09h00 – 09h40 Joachim Hermsdörfer (TU Munich) KEYNOTE Deficits of tool use following stroke: Neural correlates and technological approaches to assist in activities of daily living

09h40 – 10h00 Ted Milner (McGill U, CRIR Montreal) E02T Coordination of grip force and load force during submovements in normal and post-stroke subjects

10h00 – 10h20 Margaret Duff (RIC) E03 A portable, low-cost system for evaluating hand function during natural movement


10h50 – 11h20 Derek Kamper (Illinois Institute of Technology, RIC) KEYNOTE Neurological interactions among thumb and fingers

11h20 – 11h40 Alejandro Melendez-Calderon (Hocoma, Northwestern U) E06T Assistance and rehabilitation of hand function using a robotic glove

11h40 – 12h00 Arno Stienen (U Twente) E09T Symbionic hand orthoses for Duchenne and stroke

12h00 – 12h20 CSF Junior Award ceremony & closing words

12h30 – 14h30 Lunch


TALKS Section Page

CORTICAL CONTROL 13

COGNITIVE & CLINICAL NEUROSCIENCE 24

NEUROPROSTHETICS 37

HAPTICS & DEXTERITY 43

NEUROREHABILITATION 53


CORTICAL CONTROL Monday, September 8, 2014

09h00 – 09h20 Welcome address

09h00 – 10h00 Roger Lemon (U College London) KEYNOTE Corticospinal systems for movement generation and action observation

10h00 – 10h20 Thomas Brochier (CNRS Marseille) A13T Mapping horizontal cortical connections in primate motor cortex using intracortical micro-stimulation

10h20 – 10h50 Coffe

10h50 – 11h20 Guiseppe Luppino (U Parma) KEYNOTE Cortical circuits for purposeful hand actions

11h20 – 12h00 Marc Schieber (U Rochester) KEYNOTE Spatiotemporal distribution of object versus location in kinematics, EMG and motor cortex activity during reach, grasp and manipulation

12h00 – 12h20 Rijk Intveld (German Primate Center) A07T Grasp force coding in F5 and AIP in a delayed grasping task

12h30 – 14h30 Lunch


14h50 – 15h30 Marco Santello (Arizona State U) KEYNOTE Sensorimotor mechanisms for control and learning of dexterous manipulation

15h30 – 15h50 Marco Davare (U College London) A05T Effect of visuo-haptic conflicts on grasping movements in virtual reality

15h50 – 16h20 Coffe


16h40 – 17h20 Jörn Diedrichsen (U College London) KEYNOTE The cortical representation of hand movements

17h20 – 17h40 Anne-Dominique Gindrat (U Fribourg) A10T Effect of primary motor cortex lesion on cortical processing of tactile finger stimulation in adult monkeys: an EEG study

17h40 – 18h00 Miriam Schrafl-Altermatt (Balgrist University Hospital) A15T Neural Coupling of Cooperative Hand Movements


20h30 – Poster session (Sala Balint)

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KEYNOTE

Corticospinal systems for movement generation and action observation

R. N. Lemon1*

1 UCL Institute of Neurology, Queen Square, London, UK

Abstract The corticospinal tract is derived from multiple regions of the cerebral cortex and through its

descending collaterals and terminations makes connections with multiple levels of the sensorimotor

system, and exerts a wide degree of influence over different spinal circuits. Although all mammals

possess a corticospinal tract, this system actually shows a remarkable degree of variation across

species, which probably reflects the relative importance to those species of the different functions to

which it can contribute. Our recent analysis suggests that existing neurophysiological knowledge of

the corticospinal system is restricted to the 3-5% of the fibres which are relatively fast-conducting [2].

The functions of the huge majority of the fibres, conducting below 5 m/s, are so far unstudied, which

should be a real challenge for the future!

In primates, the fast corticospinal projections from the primary motor cortex have long been implicated

in the generation of movement, and a large body of work has shown strong relationships between

corticospinal discharge and various parameters of movement, such as the force and direction. It has

also been possible to show direct causal effects of corticospinal activity on motor output.

Nevertheless, it is clear that the motor cortex can also be active during processes that do not require

movement generation per se. These include mental rehearsal of motor acts and, intriguingly,

observation of the actions of others. Recent work shows that even M1 corticospinal neurons are active

during action observation, that is, they behave like ‘mirror neurons’ [1]. The study of the discharge of

identified corticospinal neurons during action observation and during action execution by the monkey

itself provides some clues as to what distinguishes the level of corticospinal activity normally

associated with the generation of a voluntary movement. It also reveals the key role of suppression of

corticospinal neuron activity during certain types of movement, observed and executed.

Funding: The Wellcome Trust, UCL Grand Challenge Scheme

References [1] Vigneswaran G, Philipp, R, Lemon, RN and Kraskov, A (2013) M1 corticospinal mirror neurons and their role

in movement suppression during action observation. Current Biology 23, 236-243.

[2] Firmin L, Field P, Maier MA, Kraskov A, Kirkwood PA, Nakajima K, Lemon R. N. and Glickstein M. Axon diameters and conduction velocities in the macaque pyramidal tract. J Neurophysiol (in press).

Short Biography After a PhD in England, I have worked in primate labs in Melbourne, Rotterdam, Cambridge and finally

London. My main research interest is the control of skilled hand movements by the brain and is

prompted by the need to understand why hand and finger movements are particularly affected by

damage to the cortex and its major descending pathways, for instance as a result of stroke or in spinal

injury. I have worked with amazing people and trained amazing students. Although I am now partly

retired, I am still very much involved and am now in my 40th year of running a monkey lab! I am

actively engaged in the public dialogue on the responsible use of animals in biomedical research.


A13T

Mapping horizontal cortical connections in primate motor cortex using intracortical micro-stimulation

T. Brochier1*, Y. Hao

1, A. Riehle

1,2,3

1 INT, UMR7289 Aix-Marseille Université, CNRS, Marseille, France

2 RIKEN Brain Science Institute, Wako-Shi, Japan

3 Inst of Neuroscience & Medicine (INM-6), Research Center Jülich, Jülich, Germany

Abstract Distant cortical points of the motor cortex are interconnected through long range axon collaterals of

pyramidal cells [1]. However, the functional properties of these intrinsic synaptic connections and their

spatial organization are still debated. We mapped the horizontal connections between distant cortical

sites by combining single unit recording and single pulse intracortical microstimulation (spICMS) using

Utah arrays chronically implanted in the motor cortex of two rhesus monkeys. At each electrode one

by one, spICMS was applied for 5 minutes at low frequency (10 Hz) and fixed intensity (40 µA). During

stimulation, the evoked effects were recorded on all the other electrodes of the array. We measured

the response of isolated single units by computing peri-stimulus time histogram (PSTH) triggered on

spICMS. We analyzed how the excitatory or inhibitory nature of the response, its latency and its

strength modulated in relation to the distance and location of the stimulating electrode. Significant

responses to spICMS could be evoked from long distances up to 5 mm, but the most powerful effects

were evoked by stimulation applied within 1 to 2 mm around the recording electrode. The strength of

the response decreased and the latency increased with the distance between the stimulating and

recording electrodes. Notably, the excitatory effects were broadly distributed in space whereas the

inhibitory effects were restricted to a smaller area (around 2,5mm) around the recording electrode.

These results suggest that depending on their excitatory or inhibitory properties, the horizontal

connections have different range of influence. We also discuss how this organization may relate to the

spatial organization of the motor cortex revealed by motor responses evoked by train of ICMS to or by

sensory receptive field testing. These observations provide a unique insight into the topological

organization of intrinsic cortical connections in the motor cortex.

This work was partly supported by Helmholtz Alliance on Systems Biology, European Union (FP7-ICT-

2009-6, BrainScales), Collaborative Research Agreement Riken-CNRS, ANR GRASP, CNRS (PEPS,

Neuro_IC2010) and INM6, Jülich Forschungszentrum (Pr Sonja Grün).

References [1] Capaday C, Ethier C, Van Vreeswijk C, Darling WG. On the functional organization and operational principles

of the motor cortex. Front Neural Circuits. 2013 Apr 18;7:66.

Short Biography Thomas Brochier has been studying motor control in human and animal models over the past 15

years. Following two post-docs in Allan Smith (Université de Montréal) and Roger Lemon (UCL) labs,

he moved to Marseille to study the cortical control of hand movements in non-human primates. He has

some a strong expertise in using multielectrode recordings and invasive electrical stimulation

techniques to probe the motor system at the local and network levels.


KEYNOTE

Cortical circuits for purposeful hand actions

G. Luppino1*

1 Dipartimento di Neuroscienze, Sezione di Fisiologia, Università di Parma; Via Volturno 39, I-43100 Parma, Italy

Abstract Highly evolved neural mechanisms allow primates to use their hands as very powerful tools for

interacting with the environment. Previous research has highlighted the primary role of specific

parieto-premotor cortical circuits in sensorimotor transformations for grasping, usually meant as an

unconscious process in which sensory coding of objects features automatically triggers hand motor

programs. However, until recently, the neural substrate for perceptual and cognitive control of hand

actions has been poorly understood.

In this lecture, I will review research in which, by combining anatomical with functional data, we have

aimed to define the cortical circuits of the macaque brain involved in selecting, generating and

controlling hand actions.

Specifically, I will first briefly summarize the available evidence for a crucial role of parieto-frontal

circuits linking inferior and opercular parietal areas with rostral ventral premotor (PMv) subdivisions in

sensorimotor transformations for grasping and for a role of the PMv area F5p in putting into action

grasping motor programs through projections to M1 and subcortical motor centers. Then, I will focus

on more recent connectional evidence showing that inferior/opercular parietal-PMv circuits involved in

sensorimotor transformations for grasping are at the core of a cortical network including specific

temporal and ventrolateral prefrontal sectors involved in object recognition and executive functions,

respectively.

Based on these data and on the available functional evidence, I will suggest that this network,

designated as “lateral grasping network”, is a possible substrate for integration of cognitive with hand-

related sensorimotor processes. This integration could underlie the control of hand actions based on

behavioral goals and memory-based or contextual information, and the role of sensorimotor signals in

cognitive motor functions.

These data can be useful for building up more comprehensive, anatomically plausible, models of

control of voluntary motor behavior for guiding anatomical and functional investigations in humans and

for supporting theoretically sound, culturally sensitive, research–based clinical practices.

References [1] Gerbella M, Borra E, Tonelli S, Rozzi S, Luppino G. (2013) Connectional heterogeneity of the ventral part of

the macaque area 46. Cereb Cortex, 23:967-987.

[2] Borra E, Gerbella M, Rozzi S, Luppino G. (2011) Anatomical evidence for the involvement of the macaque ventrolateral prefrontal area 12r in controlling goal-directed actions. J Neurosci. 31:12351-12363.

Short Biography Professor of Physiology at the School of Medicine of the University of Parma and Director of the

Department of Neuroscience. Medical Degree (cum laude) in 1982 and Degree in Neurology in 1986,

both at the University of Parma. PhD in Neurological Sciences in 1988. Visiting scientist at the Dept. of

Psychology, Duke University, Durham, NC, USA (1985). Visiting scientist at Dept of Physiology, Nihon

University (1992). More than 60 papers published in international peer-reviewed journals (Sum of the

Times Cited: 7754; h-index: 41; Web of Science). Member of the Editorial Board of Brain Structure

and Functions and of Frontiers in Neuroanatomy.

Main scientific interests in the field of anatomical and functional organization of the primates cortical

motor system.


KEYNOTE

Spatiotemporal distribution of object versus location in kinematics, EMG and motor cortex activity during reach, grasp

and manipulation

A. Rouse1,2,3

, M. H. Schieber1,2,3

*

1 Department of Neurology

2 Department of Neurobiology & Anatomy

3 Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA

Abstract As we reach to, grasp, and then manipulate an object, our hand must be transported to the correction

location, and shaped to grasp the object. Whereas reaching to a given location and shaping the hand

to grasp commonly are thought to proceed simultaneously but independently, a number of studies

have indicated that these two processes may not be simply parallel and independent. Here we tested

the hypothesis that reaching and grasping are inter-dependent. We examined the effects of different

locations and objects on joint angles, EMG activity, and neuron spikes in the primary motor cortex

(M1) as subjects reached, grasped and manipulated.

Individual joint angles, muscles, and spike recordings, whether proximal or distal, often showed

significant variation depending on both location and object. Nevertheless, kinematics, EMG, and

spikes each varied in two phases. The first phase peaked shortly after movement onset and showed

variation that depended somewhat more on location than on object. The second phase peaked just

before object contact and depended largely on the object about to be grasped. Each phase involved

both proximal and distal joints and muscles, as well as neurons widely distributed in the M1 upper

extremity representation. Interestingly, the activity of many EMG recordings and M1 spikes, though

peaking during each phase, increased from before movement onset until just prior to object contact.

Hence the amplitude of later, object-related activity often was larger than that of the earlier, location-

related activity.

Our findings suggest that the seamless execution of reach to and grasp of different objects in different

locations actually utilizes two sequential phases of activity, the first more location-related and the

second more object-related. Both phases require simultaneous variation in proximal and in distal

muscles and joints. Control therefore requires sequential processing by the same neurons, distributed

widely in the M1 upper extremity representation.

Short Biography Adam Rouse received his B.S. and Ph.D. degree in biomedical engineering as well as his M.D. from

Washington University in St. Louis. His doctoral work with Daniel Moran examined

electrocorticographic brain-computer interface design. He is currently a post-doctoral fellow with Marc

Schieber at the University of Rochester. His research interests include cortical control of integrated

arm, hand, and finger movements, as well as brain-machine interface development.. Marc H.

Schieber also received his A.B., Ph.D and M.D. from Washington University in St. Louis. He currently

is a Professor of Neurology, Neurobiology, and Biomedical Engineering at the Unversity of Rochester.


A07T

Grasp force coding in F5 and AIP in a delayed grasping task

R. W. Intveld1*, H. Scherberger

1,2

1 Deutsches Primatenzentrum GmbH, 37077 Göttingen, Germany

2 Department of Biology, University of Göttingen, D-37077 Göttingen, Germany

Abstract Studies focusing on the neural representation of hand forces have traditionally targeted the primary

motor cortex (M1) due to its direct connections to the corticospinal tract. Few studies have also looked

at premotor areas, where a stronger representation of movement planning is found. In this study we

focused on the macaque ventral premotor cortex, also known as area F5, because of its strong

relation to grasping movements, and at the anterior intraparietal area (AIP) that is directly connected

to F5. AIP is highly active during the planning and execution of grasping movements, but its role in the

control of grasp force is virtually unknown.

We trained a macaque monkey on a delayed grasping task, in which a manipulandum (handle) was

grasped with the right hand with two grip types, a power and a precision grip. Every grip had to be

held for 1 second at one out of three force levels. Both the particular grip type and the required

amount of force were cued to the monkey in the beginning of each trial. We then recorded neural

activity from F5 and AIP in the left hemisphere, contralateral to the moving arm, with chronically

implanted floating microelectrode arrays (FMAs). Two 32-channel FMAs were implanted in each area

(total of 128 electrodes).

We found that single unit activity in F5 and AIP was strongly modulated by both grip type and grasping

force. Response to grip type was similar in both areas. However, grasping force was more strongly

coded in F5 than in AIP during most epochs of the task. Only during the holding phase, when the

monkey was actively maintaining the force level, AIP neurons were also strongly force-modulated.

These preliminary results demonstrate a clear, but potentially different involvement of AIP and F5 in

the planning and execution of grasp forces.

References [1] Hendrix, C.M., Mason, C.R., and Ebner, T.J. (2009). Signaling of grasp dimension and grasp force in dorsal

premotor cortex and primary motor cortex neurons during reach to grasp in the monkey. J. Neurophysiol. 102, 132–145.

[2] Hepp-Reymond, M., Kirkpatrick-Tanner, M., Gabernet, L., Qi, H.X., and Weber, B. (1999). Context-dependent force coding in motor and premotor cortical areas. Exp Brain Res 128, 123–133.

Short Biography I obtained my Master degree at Utrecht University, where I studied the single unit activity in the

macaque prefrontal cortex during a working memory task in the Van Wezel lab. As part of my Master’s

program I went to the Hatsopoulos lab at the University of Chicago, where I studied the role of

feedback on the real-time control of a brain-machine interface. After my Master I started at the

German Primate Center in Göttingen, where I am currently working on a PhD project that investigates

grasping representations in the macaque frontal and parietal cortex.


KEYNOTE

Sensorimotor mechanisms for control and learning of dexterous manipulation

M. Santello1*

1 School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ

Abstract Anticipatory control of movement has been characterized in motor tasks as a way through which the

central nervous system can bypass delays associated with reflex-based control. We have been

studying how humans learn anticipatory control of manipulation tasks to characterize the mechanisms

underlying the transformation from multiple sources of sensory feedback to the coordination of multiple

degrees of freedom of the hand. In our approach, we have removed constraints on digit placement to

study how subjects explore and choose relations between digit forces and positions. The main

difference between grasp control at constrained vs. unconstrained contacts is that anticipatory control

of grasping in the former scenario can rely on sensorimotor memory of digit forces built through

previous manipulations. In contrast, trial-to-trial variability of digit placement associated with grasping

at unconstrained contacts limits the extent to which force planning can rely on sensorimotor memories

of digit forces and would require integration of digit position feedback.

I will review recent work from my laboratory on the problem of digit position/force coordination during

learning of dexterous manipulation and using tasks that allow, or interfere with, the retrieval of learned

manipulations. A key concept is that subjects build high-level task representations that allow control of

manipulation in an effector-independent fashion. The extent to which these representations can be

successfully used depends on several factors, including the frame of reference in which manipulations

are learned, time-dependent motor bias from based on most recent hand-object interactions, and

potential conflicts that may arise between visual cues versus implicit knowledge of object dynamics.

Sensorimotor memories can facilitate or interfere with the coordination of multiple degrees of freedom

of the hand for dexterous manipulation. This framework is helping to identify the neural mechanisms

underlying learning and generalization of complex movements characterized by high-dimensionality in

the sensory and motor domains.

References [1] Fu, Q, Santello, M (2014). Coordination between digit forces and positions: interactions between anticipatory

and feedback control. Journal of Neurophysiology 111, 1519-1528.

[2] Fu Q, Santello M (2012). Context-dependent sensorimotor memory interferes with visuomotor transformations for manipulation planning. Journal of Neuroscience 32:15086-15092.

Short Biography Marco Santello received a Bachelor in Kinesiology from the University of L'Aquila, Italy, in 1990 and a

Doctoral degree in Sport and Exercise Science from the University of Birmingham (U.K.) in 1995. After

a post-doctoral fellowship at the Department of Physiology (now Neuroscience) at the University of

Minnesota, he joined the Department of Kinesiology at Arizona State University (ASU) (1999-2010).

He is currently Professor of Biomedical Engineering, Director, and Harrington Endowed Chair at the

School of Biological and Health Systems Engineering. His main research interests are motor control,

learning, and biomechanics of object grasping and manipulation, haptics, and multisensory integration.


A05T

Effect of visuo-haptic conflicts on grasping movements in virtual reality

A. Nuruki1,2

, M. Davare1*

1 Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square,

London WC1N 3BG, UK 2

Faculty of Engineering, Kagoshima University, Kagoshima, Japan

Abstract Grasping and manipulating objects require the brain to extract useful information from multiple sensory

sources, in particular vision and haptics. When lifting objects, fingertip forces rely on the integration of

a sensorimotor memory acquired from previous visuo-haptic experience and online visual cues [1].

However, how the cortical grasping circuit [2] combines vision to haptics with a dynamic gain during

planning and execution of grip-lift movements is still unknown. Here we used conflicts between vision

and haptics to test their relative gain in biasing force planning for the next lift. Subjects (n=12)

interacted with a virtual reality environment to grasp haptic objects simulated by two Phantom robots

while they received online visual feedback via a 3D screen. Object size (2 or 7 cm height) and weight

(1 or 3.5 N) were varied pseudoramdomly. In 20% of trials, size and weight were incongruent (i.e.

small-heavy or large-light objects). We quantified grip force rate peak (GFr) as a behavioural read-out

of force planning. We also used transcranial magnetic stimulation (TMS) to test the gain of the effect

of vision and haptics on corticospinal excitability.

As expected, we first found that GFr was significantly higher (23% increase) for large objects

compared to small ones, irrespective of the size or weight of previous objects. Interestingly, a visuo-

haptic conflict in the previous trial biased the sensorimotor memory effect. GFr was significantly lower

(11% decrease) when the previous object was large-light compared to small-light. Conversely, there

was a significant increase in GFr (16%) when the previous object was small-heavy compared to large-

heavy. Corticospinal excitability changes paralleled behavioural effects on force planning.

These results show that the cortical grasping circuit can rapidly adapt to a new mapping between size

and weight by tuning its corticospinal output and biasing the relative effect of vision and haptics on

sensorimotor memory.

References [1] Loh, M.N., Kirsch, L., Rothwell, J.C., Lemon, R.N. & Davare, M. Information about the weight of grasped

objects from vision and internal models interacts within the primary motor cortex. J Neurosci 30, 6984-6990 (2010).

[2] Davare, M., Kraskov, A., Rothwell, J.C. & Lemon, R.N. Interactions between areas of the cortical grasping network. Curr Opin Neurobiol 21, 565-570 (2011).

Short Biography In 2008, Marco Davare obtained a PhD in Neuroscience from Université catholique de Louvain

(Belgium). He then joined the Institute of Neurology at University College London (United Kingdom) as

a post-doctoral fellow in Prof. Roger Lemon’s laboratory. The exposure to single unit studies of

premotor-motor interactions in non-human primates led him to pioneer transcranial magnetic

stimulation techniques for investigating cortico-cortical connectivity in humans. He received the

Magstim Young Investigator Award. In 2012, Marco Davare became a principal investigator at the

Institute of Neurology, focusing his research on how the brain integrates multiple sensory inputs to

control skilled hand movements.


KEYNOTE

The cortical representation of hand movements

J. Diedrichsen1*, N. Ejaz

1

1 Institute of Cognitive Neuroscience, University College London

Abstract The production of finger movements is directly controlled by the population activity of neurons in the

hand knob area of the primary motor cortex (M1). Experiments involving micro-stimulation and single-

neuron electrophysiology strongly suggest that it is not single finger movements, but rather co-

articulated gestures of the hand, that are represented cortically. However, we still do not understand

the underlying principles that shape these representations. Using functional magnetic resonance

imaging (fMRI), we analyzed the neural activation patterns associated with the production of

movements involving single and multiple fingers. While the exact spatial shape of these activity

patterns differed widely across individuals, the relative similarity of the patterns – i.e. their

representational structure - was highly invariant across individuals. This similarity relationship between

fingers does not result from the co-activation of muscles required to produce movement, but instead,

is best explained by the way we use our hands in everyday life. I will also present new data on how

the structure of these representations changes through short-term training and how it is altered in

clinical conditions, such as stroke and focal dystonia.

References [1] Diedrichsen J, Wiestler T, Krakauer JW (2013) Two distinct ipsilateral cortical representations for individuated

finger movements. Cereb Cortex 23:1362-1377.

Short Biography Dr. Jörn Diedrichsen received a PhD in cognitive neuroscience from the University of California,

Berkeley and worked as a postdoctoral fellow with Prof. Reza Shadmehr at Johns Hopkins University,

Baltimore. In 2009, he started as group leader at the Institute of Cognitive Neuroscience of the

University College London, UK. His research group uses a combination of robotics, functional brain

imaging, brain stimulation techniques and computational methods to uncover the representation of

motor skills in the human brain in health and disease. He received an APA early career award and a

James S. McDonnell scholar award for his work.


A10T

Effect of primary motor cortex lesion on cortical processing of tactile finger stimulation in adult monkeys: an EEG study

A.-D. Gindrat1*, E. M. Rouiller

1, A. Ghosh

2,3

1 Domain of Physiology, Dep. of Medicine, University of Fribourg, Fribourg, Switzerland

2 Institute of Neuroinformatics, University of Zürich and ETH Zurich, Zürich, Switzerland

3 Neuroscience Center Zurich, University of Zürich and ETH Zurich, Zürich, Switzerland

Abstract Tactile information from the fingertips is crucial for motor control underlying manual dexterity [1]. How

these inputs are integrated in sensorimotor processing is not entirely clear. Using the high temporal

resolution and non-invasiveness of high-density scalp EEG (32 channels) [2], the goal was to study

how tactile information processing from the fingertips is affected by unilateral lesion of the hand area

in the primary motor cortex (M1) in non-human primates. The neuronal activity elicited by tactile

stimulation of the fingertips was assessed in one macaque monkey before and then at several time

points (5-week interval) after lesion. Tactile stimulations (2-ms duration) were applied with solenoid

tappers over the thumb, index finger and middle finger tips. Pre-lesion, the electrodes over the

contralateral somatosensory cortex exhibited a prominent positive peak at 30 ms post-stimulus. The

theoretical sum of the individual peaks associated with separate stimulation of the thumb and index

finger was substantially larger than the peak obtained after simultaneous stimulation of both fingers, in

line with the notion of inhibitory sensory interaction between neighboring fingers. The M1 lesion

substantially altered the cortical responses to tactile stimulation. For instance, in response to thumb

stimulation, positive peaks were recorded from the frontal electrodes pre- lesion, which turned into

prominent negative peaks post-lesion. Several posterior electrodes were considerably more

responsive post-lesion than pre-lesion. Nevertheless, in spite of these changes in signal polarities and

amplitudes, the inhibitory interactions were preserved post-lesion. In conclusion, normal tactile

sensory processing is modulated by motor cortical outputs. These alterations in sensory processing

may contribute to the motor deficits observed after M1 lesion and appear to evolve in parallel with the

functional recovery for a manual dexterity task executed in absence of visual control, relying mostly on

tactile feedback.

References [1] Gindrat, A.D., Quairiaux, C., Britz, J., Brunet, D., Lanz, F., Michel, C.M., and Rouiller, E.M. (2014). Whole-

scalp EEG mapping of somatosensory evoked potentials in macaque monkeys. Brain Structure and Function (in press).

[2] Smith, A.M. (2009). The neurohaptic control of the hand. In Sensorimotor Control of Grasping: Physiology and Pathophysiology, D.A. Nowak, and J. Hermsdörfer, eds. Cambridge University Press, pp. 178-192.

Short Biography I studied Biology at the University of Fribourg (MSc in 2010), with a particular interest in Neuroscience.

I have been a student in the lab of Prof. Eric Rouiller since 2008. I am now a PhD student and I am

working on the reorganisation of the sensorimotor system following a motor cortex lesion in macaque

monkeys. We developed on the one hand whole-scalp EEG mapping of somatosensory evoked

potentials. On the other hand, we designed a behavioural task to specifically challenge the

somatosensory component of the sensorimotor system.


A15T

Neural Coupling of Cooperative Hand Movements

M. Schrafl-Altermatt1*, V. Dietz

1

1 Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland

Abstract The neural control of “cooperative” hand movements reflecting “opening a bottle” was explored in

human subjects by electromyographic (EMG) and functional magnetic resonance imaging (fMRI)

recordings. EMG responses to unilateral nonnoxious ulnar nerve stimulation were analyzed in the

forearm muscles of both sides during dynamic movements against a torque applied by the right hand

to a device which was compensated for by the left hand. For control, stimuli were applied while task

was performed in a static/isometric mode and during bilateral synchronous pro-/supination

movements. During the dynamic cooperative task, EMG responses to stimulations appeared in the

right extensor and left flexor muscles, regardless of which side was stimulated. Under the control

conditions, responses appeared only on the stimulated side. fMRI recordings showed a bilateral extra-

activation and functional coupling of the secondary somatosensory cortex (S2) during the dynamic co-

operative, but not during the control, tasks. This activation might reflect processing of shared

cutaneous input during the cooperative task. Correspondingly, it is assumed that stimulation-induced

unilateral volleys are processed in S2, leading to a release of EMG responses to both fore- arms. This

indicates a task-specific neural coupling during cooperative hand movements. This neural coupling

has further been investigated in post-stroke subjects. While stimulation of the unaffected arm led to

the same responses observed in healthy volunteers, stimulation of the affected arm did not elicit any

responses. These findings indicate a defective neural coupling after stroke which has implications on

stroke rehabilitation.

References [1] Dietz, V, Macauda G, Schrafl-Altermatt, M, Wirz, M, Kloter, E, Michels L (2013) Neural coupling of cooperative

hand movements: A reflex and fMRI study. Cerebral Cortex Advance Acess October 2013.

[2] Schrafl-Alterrmatt M, Dietz V (2014) Cooperative hand movements in stroke patients: Impaired neural coupling. Neurology, Under Review

Short Biography Miriam Schrafl-Altermatt (born December 23rd 1986, married in May 2012, mother to a son born

November 28th 2013) did her Bachelor’s (2006-2009) and Master’s (2009-2011) in Human Movement

Sciences and Sport at ETH Zurich, Switzerland. Her research interest during her master’s was the

effect of transcutaneous spinal direct current stimulation on spinal circuitries in paraplegic patients. For

her ongoing PhD (started August 2012) she investigates recovery of upper limb motor function after

stroke. In particular, she focusses on the differences between coupled and uncoupled bimanual

movements and establishes a rehabilitative training with a new device which enables these particular

movements.



NEUROSCIENCE Tuesday, September 9, 2014

09h00 – 09h40 Susan Goldin-Meadow (U Chicago) KEYNOTE From action to abstraction: Gesture as a mechanism of change

09h40 – 10h20 Georg Goldenberg (Bogenhausen Hospital Munich, TU Munich) KEYNOTE Apraxia tears apart the neural substrates of instrumental and communicative functions of the hand


10h50 – 11h20 Nicole Wenderoth (ETH Zurich, KU Leuven) KEYNOTE Making and breaking motor memories

11h20 – 11h40 Noëmi Eggenberger (University Hospital Bern) B04T Visual exploration of co-speech hand gestures in aphasic patients: An eye-tracking study

11h40 – 12h00 Silvio Ionta (ETH Zurich, CHUV) B01T Neuroplastic sensorimotor adaptations after spinal cord injury

12h00 – 12h20 Zdravko Radman (Zagreb, U Split) B06T The “enhanded” mind: An attempt for a reconception of agency

12h30 – 14h30 Lunch

14h30 – 15h10 Angela Sirigu (CNRS Lyon) KEYNOTE Varieties of movement representations in the human brain

15h10 – 15h50 Andrea Serino (EPFL, U Bologna) KEYNOTE Neural mechanisms, functions and plasticity of peripersonal space representation in humans

15h50 – 16h20 Coffe

16h20 – 17h00 Tamar Makin (U Oxford) KEYNOTE Bridging the gap between cortical reorganisation and rehabilitation in arm amputees

17h00 – 17h20 Elena Rusconi (U Parma) B12T Neural correlates of finger gnosis

17h20 – 17h40 Stefan Vogt (U Lancaster, U Liverpool) B03T Imitation learning of spatial sequences and rhythms: A FMRI study in musically naïves and drummers

17h40 – 18h00 Jennifer Gurd (U Oxford) B07T Hand, brain, attention



20h30 – 21h30 Frank Wilson (Stanford U, Professor Emeritus) KEYNOTE "The Hand: How its use shapes the brain, language, and human culture"

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KEYNOTE

From action to abstraction: Gesture as a mechanism of change

S. Goldin-Meadow1*

1 Department of Psychology, University of Chicago

Abstract The spontaneous gestures that people produce when they talk have been shown to reflect a speaker’s

thoughts––they can index moments of cognitive instability and reflect thoughts not yet found in

speech. Gesture can go beyond reflecting though to play a role in changing that thought––the

gestures we see other produce can change our thoughts, and the gestures we ourselves produce can

change our thoughts. In this talk, I consider whether gesture effects these changes because it itself is

an action and can thus bring action into our mental representations. But gesture is a special kind of

action––it spatializes ideas, even ideas that are inherently non-spatial, and it is representational and

thus more abstract than direct action on objects. Gesture’s representational properties may thus allow

it to play a role in learning by facilitating the transition from action to abstraction.

References [1] Goldin-Meadow, S. How gesture works to change our minds. Trends in Neuroscience and Education (TiNE),

2014, doi: 10.1016/j.tine.2014.01.002.

[2] Ping, R., Goldin-Meadow, S., & Beilock S. Understanding gesture: Is the listener's motor system involved? Journal of Experimental Psychology: General, 2014, 143(1), 195-204, doi: 10.1037/a0032246.

Short Biography Susan Goldin-Meadow is the Beardsley Ruml Distinguished Service Professor in the Departments of

Psychology and Comparative Human Development, and the Committee on Education at the University

of Chicago. Her research focuses on the home-made gestures profoundly deaf children create when

not exposed to sign language, and the gestures hearing speakers around the globe spontaneously

produce when they talk, with an eye toward what gesture can tell us about how we talk and think.


KEYNOTE

Apraxia tears apart the neural substrates of instrumental and communicative functions of the hand

G. Goldenberg1*

1 Department of Neuropsychology Bogenhausen Hospital Munich;

Department of Neurology, Technical University Munich

Abstract Apraxia is a disorder on the border between cognition and motor control. It is predominantly caused by

left brain damage and frequently associated with aphasia. Apraxia manifests itself in 3 domains of

manual actions: Imitation of gestures, performance of communicative gestures, and use of tools and

objects. A traditional model of apraxia ascribes disturbances of these domains to interruptions of

consecutive stages in a unique stream of action control. This postulate is refuted by double

dissociations between manifestations that should have their origin at the same step of the stream.

My contribution will concentrate on pantomime of tool use. Pantomime of tool use is a communicative

gesture. It does not alter the state of external objects but can communicate the identity of the tool and

the manner of its use to another person. Intuitively pantomime may appear as an empty handed

replication of the motor program of real use, but close observation and kinematic measurement of real

use and pantomime reveal important differences. Moreover, many patients with disturbed pantomime

can use the same tools flawlessly while, on the other hand, most people can pantomime the use of

tools which they are unable to handle in reality, like playing a violin. There is a closer association of

pantomime with other communicative gestures and with language. In right handed patients

disturbance of pantomime is regularly associated with aphasia. However, the association is not

mandatory as there are single cases of left handed patients who have disturbed pantomime but

preserved language.

Lesion analysis by means of voxel based lesion symptom mapping reveals the neural basis of the

association between pantomime and language. In the temporal lobe there is overlap between the

location of pantomime disturbance and of language comprehension. Presumably the common function

underlying this overlap is the need to retrieve knowledge from semantic memory. By contrast, parietal

lesions associated with defective use of real tools are not major sources of pantomime deficits.

Short Biography Georg Goldenberg was trained as a clinical neurologist in Vienna. He was habilitated in 1986 with a

thesis on the neurological basis of visual imagery. Since 1995 he is head of the Department for

Neuropsychological Rehabilitation at the Bogenhausen Hospital in Munich and affiliate professor of

the Technical University Munich. He published papers on different aspects of apraxia in Brain, Journal

of Neuroscience, Cerebral Cortex, Neuropsychologia, Cortex, and other core journal and recently

published a monograph "Apraxia – the Cognitive Side of Motor Control" at Oxford University Press.


KEYNOTE

Making and breaking motor memories

N. Wenderoth1,2

*

1 Neural Control of Movement Laboratory, Dept. Health Sciences and Technology, ETH Zürich, Switzerland

2 Centre for Movement Control and Neuroplasticity, KU Leuven, Belgium

Abstract Skillful movements are acquired due to repeated practice and cause the formation of motor memories.

Most of these memories are powerful and long-lasting, for example, once learned how to ride a bicycle

this skill is never forgotten. During the last years, much research has been devoted to modulating

neuroplasticity within the healthy or damaged motor system.

In the first part of my talk I will show that transcranial direct current stimulation (tDCS) is an effective

tool to facilitate plasticity within the motor system of severely to moderately impaired chronic stroke

patients. In this double-blind, randomized, clinical trial, patients were assigned to a real tDCS (2mA,

bihemispheric montage) or a sham tDCS group and were stimulated while the paretic wrist extensor

was trained using a Muscle Computer Interface (MCI). Despite a short training period of 3 days,

patients that received real tDCS but not those that received sham tDCS exhibited significant functional

improvements (Fugl-Meyer Score for the upper extremities, anodal tDCS: 5.88 ± 0.7; sham: 1.5± 0.6).)

In the second part of the talk I will show how stable motor memories can be experimentally

manipulated such that they become once again labile and can be partly erased. Using mechanisms

related to memory reconsolidation, motor performance can be partly degraded if a skill is briefly

reactivated and subsequently exposed to an interfering intervention. However, whether or not an

existing motor memory can be erased depends strongly on the reactivation-interference schedule,

such that, for example, reactivation length represents a crucial boundary condition.

In summary, there are experimental tools available that enable researchers and therapists to either

make or break motor memories. These insights open new perspectives for modulating neuroplasticity

in a clinical setting.

Short Biography Nicole Wenderoth is Professor for Neural Control of Movement in the Department of Health Sciences

and Technology at ETH Zurich, Switzerland. Her lab addresses fundamental questions on how the

brain controls movement, how new memories are formed and maintained, whether the motor system

can be driven by sensory input, and how motor control interacts with cognitive functions and emotions.

Here goal is not only to better understand the human brain but also to modulate brain function using

non-invasive brain stimulation as well as new training paradigms that can be applied in healthy

individuals as well as in patients recovering from neural damage.


B04T

Visual exploration of co-speech hand gestures in aphasic patients: An eye-tracking study

N. Eggenberger1*, B. C. Preisig

1, S. Hopfner

1, R. M. Müri

1,2

1 Departments of Neurology and Clinical Research, Inselspital, University Hospital Bern, Switzerland

2 Division of Cognitive and Restorative Neurology, Department of Neurology, Inselspital, University Hospital Bern,

Switzerland

Abstract Gesturing, including co-speech gestures, is a crucial part of human communication. Aphasia as a

consequence of left hemispheric brain damage may result in impaired speech perception and

production, frequently accompanied by limb apraxia. Healthy subjects spend about 88-95% of the time

fixating a speaker’s face, while only a minority of fixations is directed at gestures [1]. It is unclear

whether aphasic patients display a similar pattern.

29 aphasic patients and 31 controls participated. Short video sequences varying in the level of

congruity between speech and gestures (congruent, incongruent and meaningless speech-gestures

combinations) were presented and subjects had to judge this congruity by keypress. A remote eye-

tracking device allowed gaze tracking and off-line analysis of parameters on predefined areas of

interest (AOIs), such as the hands and the face of the speaker.

Repeated measures ANOVAs yielded a significant interaction between the factors AOI x Group,

indicating that aphasic patients spent more time fixating the hands compared to healthy controls, while

controls fixated more on the speaker’s face compared to patients.

Aphasic patients showed a different visual exploration pattern insofar as they looked less on the

speaker’s face but more on the gesturing hands compared to controls. Aphasic patients might thus

rely more on the additional (nonverbal) information presented by gestures in order to understand

verbal utterances and to judge increasingly complex sequences. Presuming a generally reduced

information processing capacity in patients with brain lesions, it could also be assumed that the visual

attention of aphasic patients shifts unconsciously to gestural movements which attract attention.

References [1] Gullberg, M., and Holmqvist, K. (1999). Keeping an eye on gestures: Visual perception of gestures in face-to-

face communication. Pragmatics & Cognition, 7(1): 35-63.

Short Biography Noëmi Eggenberger obtained her Master’s degree in Neuropsychology at the University of Bern in

2010. She started her PhD project in Neurosciences in 2012 and is focusing on clinical research with

aphasic and apractic patients. Her research interests are the reciprocal relationship between language

and gestures and possible implications for neurorehabilitation.


B01T

Neuroplastic sensorimotor adaptations after spinal cord injury

S. Ionta1,2

*, M. Villiger3, C. Jutzeler

3, P. Freund

3, A. Curt

3,

R. Gassert1

1 Rehabilitation Engineering Laboratory, Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland

2 Laboratory for Investigative Neurophysiology, Centre Hospitalier Universitaire Vaudois (CHUV), University of

Lausanne, Lausanne, Switzerland 3

Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland

Abstract According to the “Simulation Theory”, peripheral proprioceptive information influences centrally-

processed mental transformations of bodily images. In particular, hand postural changes specifically

affect mental rotation, both at the behavioral [2] and the neural level [1]. Spinal cord injury (SCI)

determines a communication breakdown between the central and the peripheral nervous system.

However, the influence of modified bottom-up input on body-related central processing is still largely

under debate. We hypothesized that during mental rotation of visual bodily stimuli, the reduction of

sensory input in SCI patients would determine a weaker reliance on proprioceptive information as a

function of the type of lesion, and therefore on the amount of residual sensory input itself.

To test this hypothesis we elicited mental transformations of bodily images in complete and incomplete

SCI patients and healthy controls by asking them to verbally judge the laterality of visually-presented

hands, feet, and full bodies while keeping their corresponding body parts (hands, feet) either straight

or crossed. During mental transformation of feet, with respect to controls, complete paraplegic SCI

patients failed to show the typical stimulus-specific increased response times in the crossed condition.

Conversely, the effect of proprioceptive changes was preserved in the mental processing of hand

images. Incomplete SCI patients’ performance reflected the typical profile of response times, but with

increased latencies. The control condition (full body) confirmed the absence of proprioceptive effects

in both patients and controls. The present data highlight the relative weight of proprioceptive

information on mental processing of visual stimuli, show the effects of compensatory mechanisms in

the continuous updating of the body representation, and drive attention to the potential application of

the present experimental protocol in clinical assessment of central nervous system plasticity.

References [1] G de Lange, F. P., Helmich, R. C., & Toni, I. (2006). Posture influences motor imagery: an fMRI study.

Neuroimage, 33(2), 609-617.

[2] Ionta, S., Fourkas, A. D., Fiorio, M., & Aglioti, S. M. (2007). The influence of hands posture on mental rotation of hands and feet. Exp Brain Res, 183(1), 1-7.

Short Biography Silvio Ionta received the MSc in Experimental Psychology at the University of Rome “La Sapienza”,

Italy. He accomplished the PhD in Functional Neuroimaging at the Institute of Advanced Bio-Medical

Technologies, University of Pescara-Chieti “G. D’Annunzio”, Italy. Between 2008 and 2012, he worked

as post-doctoral researcher at the Laboratory of Cognitive Neuroscience, EPFL, Switzerland. In 2012,

he worked as research assistant at the Rehabilitation Engineering Lab, ETH Zurich, Switzerland. In

2014 he took up his lecturer position at the Centre Hospitalier Universitaire Vaudois and the University

of Lausanne, Switzerland.


B06T

The “enhanded” mind: An attempt for a reconception of agency

Z. Radman1,2

*

1 Institute of Philosophy, Zagreb

2 University of Split

Abstract Our culture is dominantly thought- and conscious-centered and such is also our understanding of

agency. According to it, to perform an action means to realize some (propositional) preconceived

plan. My focusing on the hand in this paper has double motivation: to show that manual perception

does not conform to the ‘intellectualist’ cannon and also to draw a possible conclusion about the

nature of agency that proves to be hand-centered in a largely autonomous way.

Once we realize that the mind is in an important way ‘enhanded’ we are in a position not only to grant

embodiment a more specific domain of application but also to give it additional signification.

After contemporary cognitive science has discovered embodiment it is easy to conceive that

technology, and specifically robotics, will not be able to avoid it either. Any such attempt will have to

face the following: on the one hand, the mind is not merely a computation processing but is in an

important sense embodied, and, on the other hand, one has to realize that the body is ‘enminded’, in

the sense that it possesses own know how that provides the cognitive organism a capacity to do more

than the thinking ‘self’ can possibly realize. It becomes particularly evident when we focus on

manipulation. We then realize that in much of our motor behavior the manual has the lead and that it is

done in an effortless and automatic way, without engaging in conscious deliberation or contemplation.

Thus what an agent can, or cannot, do is ultimately decided according to the “knowledge in the hands”

(M.Merleau-Ponty); it alone is the final arbiter on doability.

Short Biography Zdravko Radman is a Research Fellow at the Institute of Philosophy, Zagreb, and a Professor of

Philosophy at the University of Split, Croatia. As an Alexander von Humboldt and a William J. Fulbright

Fellow he was affiliated with the University of Konstanz and the University of California, Berkeley; as a

visiting scholar he conducted research at the Australian National University, the University of Tokyo,

and University College London, among others. He has published in the philosophy of mind, aesthetics,

and the philosophy of language. He is the author of Metaphors: Figures of the Mind (Kluwer, 1997,

Springer 2010), and editor of Horizons of Humanity (Peter Lang, 1997), and From a Metaphorical

Point of View (Walter de Gruyter, 1995). His most recent publications include: Knowing without

Thinking: Mind, Action, Cognition, and the Phenomenon of the Background (Palgrave Macmillan,

2012): http://us.macmillan.com/knowingwithoutthinking/ZdravkoRadman

and The Hand, an Organ of the Mind; What the Manual Tells the Mental (MIT Press, 2013):

https://mitpress.mit.edu/books/hand-organ-mind

http://us.macmillan.com/knowingwithoutthinking/ZdravkoRadman

https://mitpress.mit.edu/books/hand-organ-mind


KEYNOTE

Varieties of movement representations in the human brain

A. Sirigu1*

1 Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France

Abstract I will discuss the role of parietal and motor regions in movement representation and movement

prediction. I will present findings obtained in patients with selective lesions of the parietal or premotor

cortex using task requiring attention to onset of intention or attention to onset of movement. I will also

show how direct cortical stimulation (during neurosurgery) of the inferior parietal regions produces the

'desire to move' and at higher intensities illusion of movement even when no motor act actually occurs

as shown by EMG recording. The opposite patterns will be described during stimulation of the

premotor cortex where patients produce movements but the experience of movement doesn’t reach

consciousness. I will argue that the inferior parietal regions play a key role for anticipating the future

states of our own movements and for bringing them into awareness.

References [1] Desmurget M, Reilly KT, Richard N, Szathmari A, Mottolese C, Sirigu A. (2009) Movement intention after

parietal cortex stimulation in humans. Science, 8, 811-3.

[2] Desmurget M, Sirigu A. (2012) Conscious motor intention emerges in the inferior parietal lobule. Curr Opin Neurobiol. 6:1004-11.

Short Biography My training and core research domain is in Neuropsychology and Cognitive Neuroscience. With my

collaborators we use an array of behavioural and neuroimagery (fMRI, EEG, TMS, cortical stimulation)

techniques, to understand the functions of different brain regions. I have a longstanding interest in the

role of parietal cortex in motor functions and motor plasticity. My research also focuses on decision

making processes and on the effect of hormones such as oxytocin in the regulation of social

interaction in healthy subjects and in patients with autism.


KEYNOTE

Neural mechanisms, functions and plasticity of peripersonal space representation in humans

A. Serino1,2

*

1 Center for Neuroprosthetics, EPFL, Lausanne

2 Dipartimento di Psicologia, Università di Bologna

Abstract The space immediately surrounding the body, i.e., peripersonal space (PPS), is represented by a

dedicated neural system of fronto-parietal areas, which integrate tactile, auditory and visual stimuli

presented on or close to the body.

In this talk, I will present neuroimaging evidence showing how, in humans, premotor and posterior-

parietal areas integrating multisensory stimuli within PPS directly project to the motor system to trigger

appropriate responses. I will also show that PPS representation is plastic, as its boundaries adapt as a

function of experience (such as after tool-use) or changes in structure and function of the psychical

body, such as amputation, prosthesis implantation and immobilization. Finally, I will demonstrate that

PPS boundaries are sensitive to the presence of and interaction with other people. I will conclude that

PPS represents a multi-sensory-motor interface between the individual and the environment.

References [1] Teneggi, C., Canzoneri, E., di Pellegrino, G., & Serino, A. (2013). Social modulation of peripersonal space

boundaries. Current Biology : CB, 23(5), 406–411.

[2] Canzoneri, E., Marzolla, M., Amoresano, A., Verni, G., & Serino, A. (2013). Amputation and prosthesis implantation shape body and peripersonal space representations. Scientific Reports, Sci Rep. 2013 Oct 3;3:2844

Short Biography Andrea Serino is Senior Scientist at the Center for Neuroprothetics at the EPFL since 2012 and

Assitant Professor at the Department of Psychology, University of Bologna, since 2006.

His main research question is how the brain generates the experience of "being here and now" by

integrating multisensory information related to the body and to the space immediately surrounding the

body, i.e. Peripersonal Space. To answer this question he has been used different techniques, namely

Psychophysics, Neuropsychology, TMS, tDCS, fMRI and Neural network models.


KEYNOTE

Bridging the gap between cortical reorganisation and rehabilitation in arm amputees

T. R. Makin1*

1 FMRIB Centre, Nuffield Department of Clinical Neuroscience, University of Oxford

Abstract Amputation provides a powerful model for studying plasticity, as it involves both sensory deprivation

(associated with nerve deafferentation) and adaptive motor behavior: Following arm amputation,

individuals need to develop new strategies and motor skills to compensate for their disability. The

contribution of this considerable behavioural pressure, which is key for rehabilitation, has been largely

neglected from research of plasticity in amputees. Instead, neuroscience research has been mostly

restricted to maladaptive plasticity, with a focus on phantom pain. Here I will test the extent to which

experience relating to rehabilitation alters brain structure and function in individuals with unilateral

hand absence, using neuroimaging approaches. I will challenge the proposed link between cortical

reorganisation and phantom pain, and instead demonstrate that phantom pain is associated with

maintained representation of the missing (‘phantom’) hand. Using 7T technology I will provide new

insight into the limitations of brain plasticity, by uncovering latent digit topography of the phantom

hand, maintained decades following amputation. I will next demonstrate how preserved motor control

of the phantom hand can be exploited to experimentally induce and relieve phantom pain. Finally, I will

show that adaptive behaviour in amputees can drive plasticity well beyond the “critical period” time-

window, though such plasticity may be restricted to the deprived cortex. I will provide new evidence for

the relationship between lateralised limb-use patterns and lateralised structural and functional

plasticity. Together, these results demonstrate how experience-driven plasticity in the human brain

can transcend boundaries that have been thought to limit reorganisation after sensory deprivation in

adults. These finding could be utilized to improve future rehabilitation in amputees.

References [1] Makin TR, Cramer AO, Scholz J, Hahamy A, Henderson Slater D, Tracey I, et al. Deprivation-related and use-

dependent plasticity go hand in hand. eLife 2013; 2: e01273.

[2] Makin TR, Scholz J, Filippini N, Henderson Slater D, Tracey I, Johansen-Berg H. Phantom pain is associated with preserved structure and function in the former hand area. Nat Comms 2013; 4: 1570.

Short Biography I’m a Henry Dale Fellow at Oxford University’s brain imaging centre (FMRIB). My group studies

multisensory plasticity in body representation, using neuroimaging, psychophysics and brain

stimulation techniques. I work closely with FMRIB’s Plasticity (headed by Heidi Johansen-Berg) and

Pain (headed by Irene Tracey) groups.


B12T

Neural correlates of finger gnosis

E. Rusconi1*, L. Tamè

2, M. Furlan

3, P. Haggard

4, G. Demarchi

2, M. Adriani

2, P. Ferrari

2,

C. Braun5, J. Schwarzbach

2

1 University of Parma, Italy

2 University of Trento, Italy

3 Royal Holloway, UK

4 University College London, UK

5 Eberhard-Karls University of Tuebingen, Germany

Abstract Neuropsychological studies have described patients with a selective impairment of finger identification

in association with posterior parietal lesions. However, confirmation of the role of these areas in finger

gnosis from studies of the healthy human brain is still scarce. Here we used functional magnetic

resonance imaging (fMRI) to identify the brain network engaged in a novel finger gnosis task, the

intermanual in-between task (IIBT), in healthy participants. Several brain regions exhibited a stronger

blood-oxygenation level-dependent (BOLD) response in IIBT than in a control task that did not

explicitly rely on finger gnosis but used identical stimuli and motor responses as the IIBT. The IIBT

involved stronger signal in the left inferior parietal lobule (IPL), bilateral precuneus (PCN), bilateral

premotor cortex (PMC), and left inferior frontal gyrus (IFG). In all regions, stimulation of non-

homologous fingers of the two hands elicited higher BOLD-signal than stimulation of homologous

fingers. Only in the left antero-medial IPL (a-mIPL) and left PCN signal strength decreased

parametrically from non-homology, through partial homology to total homology with stimulation

delivered synchronously to the two hands. With asynchronous stimulation, the signal was stronger in

the left a-mIPL than in any other region, possibly indicating retention of task-relevant information. We

suggest that the left PCN may contribute a supporting visuo-spatial representation via its functional

connection to the right PCN. The a-mIPL may instead provide the core substrate of an explicit bilateral

body structure representation for the fingers that when disrupted can produce the typical symptoms of

finger agnosia.

References [1] Kinsbourne, M., Warrington, E.K. (1962). A study of finger agnosia. Brain 85, 47–66.

[2] Rusconi, E., Gonzaga, M., Adriani, M., Braun, C., Haggard, P. (2009). Know thyself: behavioral evidence for a structural representation of the human body. PLoS ONE 4, e5418.

Short Biography Elena Rusconi holds a laurea in General and Experimental Psychology, a PhD in Cognitive Sciences

(both from University of Padova) and a PhD in Security Science (from University College London). Her

basic research activity focuses on the cognitive mechanisms and the neural basis of higher functions

such as visuospatial attention, mathematical cognition and body structure representation, as well as

on the cross-talks between these domains. She uses a range of methods such as mental

chronometry, neuropsychology, functional Magnetic Resonance Imaging and Transcranial Magnetic

Stimulation. She also conducts translational research in the context of x-ray guided inspections at

security checkpoints.


B03T

Imitation learning of spatial sequences and rhythms: A FMRI study in musically naïves and drummers

K. Sakreida1,2

, S. Higuchi3,4,5

, C. Di Dio6, M. Ziessler

7, M. Turgeon

3, N. Roberts

8, G. Rizzolatti

6,

S. Vogt3,4

*

1 Section Clinical-Cognitive Sciences–Department of Neurology, Medical Faculty, RWTH Aachen University,

Aachen, Germany 2

Department of Neurosurgery, Medical Faculty, RWTH Aachen University, Aachen, Germany 3

Department of Psychology, Lancaster University, Lancaster, United Kingdom 4

Magnetic Resonance and Image Analysis Research Centre, University of Liverpool, Liverpool, United Kingdom 5

Center for Experimental Research in Social Sciences, Hokkaidō University, Sapporo, Japan 6

Department of Neuroscience, University of Parma, Parma, Italy 7

Department of Psychology, Liverpool Hope University, Liverpool, United Kingdom 8

Clinical Research Imaging Centre (CRIC) and Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh, Scotland, United Kingdom

Abstract Imitation learning involves the acquisition of novel motor patterns based on action observation.

Previous studies have demonstrated that the human mirror neuron system (MNS) is essential for

imitative learning of configural hand actions and that the MNS in interaction with the dorsolateral

prefrontal cortex (DLPFC) activate more strongly for novel as compared to practiced actions [1]. In

addition, there is evidence for a more specific involvement of the DLPFC in the acquisition of spatial

as compared to temporal patterns. We address these issues by comparing regions of functional

activation during the imitation of spatial sequences and rhythms, using event-related functional

magnetic resonance imaging.

One day before scanning with a Siemens 3T Trio, participants practiced three spatial sequences

(SEQ) and three rhythms (RHY) with their left index finger. They were then tested on these practised

(PR) as well as on non-practised (NP) patterns in three presentation conditions: Observation,

Rehearsal, and Execution.

Our results give rise to three main conclusions. First, imitation learning of spatial pattern of sequences

engaged fundamentally the same areas as imitation learning of configural hand actions [1]. Secondly,

our data revealed a clear dissociation between spatial sequence and rhythm representations (reduced

PPC, enhanced BA44, SMA, and STG). Most likely, the visually presented rhythms were recoded as

silent vocalization. Thus, according to the type of action observed, the mirror mechanisms involved

can be remarkably different. Third, whilst a restricted differential activation of right DLPFC was indeed

found during observation of novel spatial sequences (confirming [1]), practice-related prefrontal

activation differences were overall more pronounced during execution. Here, activation differences in

DLPFC were more distinct for the spatial sequences, particularly in the skilled drummers. One

possible explanation is that rhythms are encoded in a specialised subsystem which requires less

supervisory control than spatially oriented actions.

References [1] Higuchi, S., Holle, H., Roberts, N., Eickhoff, S.B., Vogt, S. (2012). Imitation and observational learning of hand

actions: prefrontal involvement and connectivity. Neuroimage, vol. 59, no. 2, pp. 1668-1683.

Short Biography Stefan Vogt is an experimental psychologist and neuroscientist and has widely published on action

observation, imitation learning and automatic imitation. He gained a diploma in Psychology at Münster

University and received his PhD at Bremen University in 1988. He then worked as a Senior

Researcher at the Max-Planck-Institute for Psychological Research in Munich, before joining the

Psychology Department at Lancaster University in 1995. His research is focussed on relationships

between perception, motor imagery, and action, and he uses a range of behavioural and neuroscience

methods, kinematic data, reaction times, and functional magnetic resonance imaging (fMRI).


B07T

Hand, brain, attention

J. Gurd1*, P. Vila

2, F. Essig

3, R. Rosch

4

1 University of Oxford, Nuffield Department of Clinical Neurosciences

2 Green Templeton College, Oxford

3 University of Hertfordshire

4 King’s College Hospital, Institute of Psychiatry, Department of Clinical Neurosciences

Abstract Recent evidence from our group (Banissy et al., 2012) has demonstrated a right space advantage

conferred on performance of a simple motor task. The significance of these findings is now further

explored with reference to handedness and cerebral asymmetry (cf. Gurd et al., 2010). Studies of

finger tapping in twins who are discordant for handedness permit computations of structure-function

mapping (Gurd et al., 2013, 2014).

Whilst the term ‘attention’ has been employed vis-à-vis hemi-spatial effects, confusion has arisen from

its simultaneous usage within the verbal domain (Essig & Gurd, 2013). It is nonetheless possible to

disentangle and clarify the debate: Using behavioural paradims of verbal and visuo-spaital attention in

monozygotic handedness discordant twins, combined with magnetic resonance imaging of the brain

(MRI, fMRI) allows the examination of within twin pair differences across tasks and their neural

substrates (Lux et al., 2008; Rosch et al., 2010).

New data is presented relevant to frameworks of verbal and visuo-spatial attention and cerebral

asymmetry of function. We examine novel relationships (hand, brain, attention) in a single twin pair

with discordant handedness and cerebral asymmetry. Notably, performance of fine motor tasks

including finger tapping (in right versus left hemi-space) and line-bisection (using right versus left

hands) will be assessed in light of attentional performance in verbal task-switching.

References [1] Banissy, M.H., Annett, L.E., Asiedu-Offei, P., Rosch and Gurd, J.M. (2012). Left, Right, Hand ‘n Space, in: J

Dunham & T Davenport (Eds.) Handedness: Theories, Genetics and Psychology, Nova Science Pub, NY, 109-122.

[2] Gurd, J.M. and Cowell, P.E. (2014) Discordant cerebral lateralization for verbal fluency is not an artifact of attention: Evidence from MzHd twins. Journal of Brain Structure & Function (in press).

Short Biography Dr JM Gurd is a cognitive neuropsychologist with a background in experimental psychology and

linguistics. She is currently a senior research associate in the Nuffield Department of Clinical

Neurosciences at the John Radcliffe Hospital, Oxford. She works together with Fiona Essig (PhD

candidate), Richard Rosch (clinical research fellow), and Pierre Vila (medical student). Their work

focuses on cerebral lateralization for verbal and visuo-spatial processes in models of health and

disease.


NEUROPROSTHETICS Wednesday, September 10, 2014

09h00 – 09h40 Andrew Schwarz (U Pittsburgh) KEYNOTE Recent progress toward a high-performance neural prosthesis

09h40 – 10h20 Sliman Bensmaia (U Chicago) KEYNOTE Biological and bionic hands: Natural neural coding and artificial perception


10h50 – 11h20 Lee Miller (Northwestern U) KEYNOTE Restoring hand function with a biomimetic neural interface and Functional Electrical Stimulation

11h20 – 12h00 Hansjörg Scherberger (German Primate Center) KEYNOTE Grasp predictions from motor, premotor, and parietal population signals

12h00 – 12h40 Todd Kuiken (RIC, Northwestern U) KEYNOTE Developing neural interfaces for powered prosthetic limbs

13h00 – 15h00 Lunch

15h00 – 21h30 Excursion & conference dinner

TA

LK

S – N

EU

RO

PR

OST

HET

ICS

(Wednesd

ay)


KEYNOTE

Recent progress toward a high-performance neural prosthesis

A. B. Schwarz1*

1 Department of Neurobiology, Motor Lab, University of Pittsburgh, Pennsylvania, USA

Abstract A better understanding neural population function would be an important advance in systems

neuroscience. Neurons encode many parameters simultaneously, but the fidelity of encoding at the

level of individual neurons is weak. However, because encoding is redundant and consistent across

the population, extraction methods based on multiple neurons are capable of generating a faithful

representation of intended movement. The realization that useful information is embedded in the

population has spawned the current success of brain-controlled interfaces. Since multiple movement

parameters are encoded simultaneously in the same population of neurons, we have been gradually

increasing the degrees of freedom (DOF) that a subject can control through the interface. Our early

work showed that 3-dimensions could be controlled in a virtual reality task. We then demonstrated

control of an anthropomorphic physical device with 4 DOF in a self-feeding task. Currently, monkeys

in our laboratory are using this interface to control a very realistic, prosthetic arm with a wrist and hand

to grasp objects in different locations and orientations. Our recent data show that we can extract 10-

DOF to add hand shape and dexterity to our control set. This technology has now been extended has

been extended to a paralyzed patient who cannot move any part of her body below her neck. Based

on our laboratory work and using a high-performance “modular prosthetic limb” she has been able to

control 10 degrees-of-freedom simultaneously. The control of this artificial limb is intuitive and the

movements are coordinated and graceful, closely resembling natural arm and hand movement. This

subject has been able to perform tasks of daily living – reaching to, grasping and manipulating objects,

as well as performing spontaneous acts such as self-feeding. Current work is progressing toward

making this technology more robust and extending the control with tactile feedback to sensory cortex.

Short Biography Dr. Schwartz received his Ph.D. in Physiology from the University of Minnesota in 1984. He then went

on to a postdoctoral fellowship with Dr. Apostolos Georgopoulos, who was developing the concept of

directional tuning and population-based movement representation in the motor cortex. He has been at

the University of Pittsburgh since 2002. Through his research, Schwartz developed a paradigm to

explore cortical signals generated during volitional arm movements. This effort showed that a high-

fidelity representation of movement intention could be decoded from the motor cortex. This has

enabled technology now being used by paralyzed subjects to operate a high-performance prosthetic

arm and hand.


KEYNOTE

Biological and bionic hands: Natural neural coding and artificial perception

S. J. Bensmaia1,2

*

1 Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL

2 Committee on Computational Neuroscience, University of Chicago, Chicago, IL

Abstract Our ability to manipulate objects dexterously relies fundamentally on sensory signals originating from

the hand. To restore motor function with upper-limb neuroprostheses requires that somatosensory

feedback be provided to the tetraplegic patient or amputee. Given the complexity of state-of-the-art

prosthetic limbs, and thus the huge state-space they can traverse, it is desirable to minimize the need

of the patient to learn associations between events impinging upon the limb and arbitrary sensations.

With this in mind, we seek to develop approaches to intuitively convey sensory information that is

critical for object manipulation – information about contact location, pressure, and timing – through

intracortical microstimulation (ICMS) of primary somatosensory cortex (S1). To this end, we first

explore how this information is naturally encoded in the cortex of (intact) non-human primates (Rhesus

macaques). In stimulation experiments, we then show that we can elicit percepts that are projected to

a specific localized patch of skin by stimulating neurons with corresponding receptive fields. Similarly,

information about contact pressure is conveyed by invoking the natural neural code for pressure,

which entails not only increasing the activation of local neurons but also recruiting adjacent neurons to

signal an increase in pressure. In a real-time application, we demonstrate that animals can perform a

pressure discrimination task equally well whether mechanical stimuli are delivered to their native

fingers or to a prosthetic one. Finally, we propose that the timing of contact events can be signaled

through phasic ICMS at the onset and offset of object contact that mimics the ubiquitous on and off

responses observed in S1 to complement slowly-varying pressure-related feedback. We anticipate

that the proposed biomimetic feedback will considerably increase the dexterity and embodiment of

upper-limb neuroprostheses and will constitute an important step in restoring touch to individuals who

have lost it.

References [1] Bensmaia, S.J. & Miller, L.E. (2014). Restoring sensorimotor function through intracortical interfaces: progress

and looming challenges, Nature Reviews Neuroscience, 15, 313-325.

[2] Tabot, G.A., Dammann III, J.F., Berg, J.A., Tenore, F.V., Boback, J.L., Vogelstein, R.J., & Bensmaia, S.J. (2013). Restoring the sense of touch with a prosthetic hand through a brain interface, Proceedings of the National Academy of Science, 110, 18279-84.

[3] Berg, J.A., Dammann, J.F., Tenore, F.V., Tabot, G.A., Boback, J.L., Manfredi, L.R., Peterson, M.L., Katyal, K.D., Johannes, M.S., Makhlin, A., Wilcox, R., Franklin, R.H., Vogelstein, R.J., Hatsopoulos, N.G., & Bensmaia, S.J. (2013). Behavioral demonstration of a somatosensory neuroprosthesis, IEEE Transactions in Neural Systems and Rehabilitation Engineering, 21, 500-507.

Short Biography I received a B.A. in Cognitive Science from the University of Virginia in 1995 and a PhD in Cognitive

Psychology from the University of North Carolina at Chapel Hill. In 2003 I joined the lab of Dr. Kenneth

Johnson at the Johns Hopkins University Krieger Mind/Brain Institute, as a postdoctoral fellow until

2006, at which time I was promoted to Associate Research Scientist. In 2009, I joined the faculty as

Assistant Professor in the Department of Organismal Biology and Anatomy at the University of

Chicago, where I also am a member of the Committees on Neurobiology and on Computational

Neuroscience. The objectives of my lab are the neural basis of somatosensory perception using

psychophysics, neurophysiology, and computational modeling. I also seek to apply insights from basic

science to develop approaches to convey sensory feedback in upper-limb neuroprostheses.


KEYNOTE

Restoring hand function with a biomimetic neural interface and Functional Electrical Stimulation

L. Miller1,2,3

*, C. Ethier1, S. Naufel

3, B. Franco

3, N. Brill

4, D. Tyler

4

1 Department of Physiology, Northwestern University

2 Physical Medicine and Rehabilitation, Northwestern University

3 Biomedical Engineering, Northwestern University

4 Biomedical Engineering, Case Western Reserve University

Abstract Functional Electrical Stimulation offers the means to restore motor function to patients suffering

paralysis due to spinal cord injury or other neurological disorders. Current FES devices must deliver

preprogrammed stimulation to the many muscles necessary to allow even simple hand movement, as

these patients lack the ability to control so many degrees of freedom using their own residual

movements. We have developed an FES prosthesis controlled by signals recorded from neurons in

the hand area of the motor cortex. We use a decoder consisting of multiple input impulse responses

computed between M1 discharge and EMG, recorded during normal movement. We subsequently

paralyze the forearm flexor muscles with a temporary peripheral nerve block, and use the decoder to

compute stimulus pulse width commands that are delivered to implanted, intramuscular electrodes.

The system essentially bypasses the spinal cord, allowing the monkeys to regain voluntary control of

the paralyzed muscles, apparently by producing approximately “normal” patterns of cortical activity.

However, implanting electrodes within the large number of muscles necessary to allow even

moderately dexterous movements is challenging. An alternative approach is to use multi-contact,

peripheral nerve electrodes, which would potentially allow activation of many muscles from a single

implant site. We are currently developing the methods necessary to control muscle activation from M1,

using Flat Interface Nerve Electrodes (FINE) including the use of adaptive methods that will not

require recorded EMG signals for decoder development.

We anticipate that such a system might ultimately provide spinal cord injured patients with control of

arm and hand movements through normal cognitive processes, and greatly enhance their

independence and well being.

References [1] Ethier C, Oby ER, Bauman MJ, Miller LE (2012) Restoration of grasp following paralysis through brain-

controlled stimulation of muscles. Nature 485:368-371

[2] Brill N, Polasek K, Oby ER, Ethier C, Miller LE, Tyler DJ (2009) Nerve cuff stimulation and the effect of fascicular organization for hand grasp in nonhuman primates. In: Engineering in Medicine and Biology Society, Minneapolis, MN, pp 1557-1560

Short Biography Lee E. Miller received a B.A. in physics from Goshen College, Goshen, IN, in 1980, an M.S. in

biomedical engineering and a Ph.D. degree in physiology from Northwestern University, Evanston, IL,

in 1983 and 1989, respectively. He completed two years of postdoctoral training in the Department of

Medical Physics, University of Nijmegen, The Netherlands. He is currently the Stuntz Distinguished

Professor of Neuroscience in the Departments of Physiology and Physical Medicine and Rehabilitation

at Northwestern University. His primary research interests are in the cortical control of limb movement,

and in the development of neural interfaces that attempt to mimic normal physiological systems.


KEYNOTE

Grasp predictions from motor, premotor, and parietal population signals

H. Scherberger1*

1 German Primate Center, Kellnerweg 4, 37077 Göttingen, Germany

Abstract Hand function plays an important role in all primate species, and its loss is associated with severe

disability. Grasping movements are complex motor acts for which the brain needs to integrate sensory

and cognitive signals to generate behaviorally meaningful actions. To achieve this computation,

specialized brain areas in the primate parietal (anterior intra-parietal area, AIP), premotor (area F5),

and primary motor cortex (M1 hand area) are functionally connected. This presentation highlights

recent experimental results in non-human primates to characterize how AIP, F5, and M1 generate

grasping movements and how such movements can be decoded from spiking activity of these areas

using permanently implanted electrode arrays while animals are grasping objects of various shape,

size, and orientation. Besides understanding the underlying network structure and function, such

characterizations are useful to evaluate the suitability of these preparatory and motor areas for the

development of neural interfaces that aim to restore hand function in paralyzed patients.

References [1] Schaffelhofer, S., Scherberger, H. (2012). A new method of accurate hand- and arm-tracking for small

primates. Journal of Neural Engineering 9:026025.

[2] Scherberger, H. (2012). BCIs that use signals recorded in parietal and premotor cortex. In: Brain-computer interfaces: principles and practice (Wolpaw JR, Wolpaw EW, eds), pp 289-299. New York: Oxford University Press.

Short Biography Hans Scherberger received his Master degree in Mathematics (1993) and his Medical Doctor degree

(1996) from Freiburg University, Germany. He currently heads the Neurobiology Lab at the German

Primate Center and is Professor for Primate Neurobiology at Göttingen University (since 2008). He

was trained in systems electrophysiology with post-doctoral positions at the University of Zurich (1995-

1998) and at the California Institute of Technology (1998-2003) before becoming a research group

leader at the Institute of Neuroinformatics at the University and ETH Zurich (2004-2009). His research

is focused on the neural coding and decoding of hand grasping movements in the primate brain.


KEYNOTE

Developing neural interfaces for powered prosthetic limbs

T. Kuiken1,2

*, G. Dumanian3, L. Hargrove

1

1 Rehabilitation Institute of Chicago, Chicago Illinois

2 Physical Medicine and Rehabilitation Dept., Northwestern Feinberg School of Medicine, Northwestern

University, Chicago, IL 3 Surgery Dept., Northwestern Feinberg School of Medicine, Northwestern University, Chicago, IL

Abstract The ability to control complex robot prostheses is evolving quickly. Dr Kuiken will describe the

research at the Center for Bionic Medicine/Rehabilitation Institute of Chicago and Northwestern

University to develop a neural-machine interface to improve the function of artificial limbs. They have

developed a technique called Targeted Reinnervation to use nerve transfers for improvement of

robotic arm control and to provide sensation of the missing hand. By transferring the residual arm

nerves in an upper limb amputee to spare regions of muscle it is possible to make new

electromyographic (EMG) signals for the control of robotic arms. These signals are be directly related

to the original function of the lost limb and allow simultaneous control of multiple joints in a natural way

[1]. This work has now been extended with the use of pattern recognition algorithms that decode the

user’s intent, enabling the intuitive control of many more functions it the prostheses [2]. Similarly, hand

sensation nerves can be made to grow into spare skin on the residual limb so that when this skin is

touched, the amputee feels like their missing hand is being touched. This is a potential port to

providing physiologically correct sensory feedback to amputees [1]. Our team is now also developing a

neural interface for powered leg prostheses that enables intuitive mobility.

References [1] Kuiken TA, Miller LA, Lipschutz RD, Lock B, Stubblefield K, Marasco P, Zhou P and Dumanian G. (2007)

Targeted Reinnervation for Enhanced Prosthetic Arm Function in Woman with a Proximal Amputation: a case study. Lancet, 369(9558): 371-80

[2] Kuiken TA, Li G, Lock BA, Lipschutz RD, Miller LA, Stubblefield KA, and Englehart K. (2009) Targeted Muscle Reinnervation for Real-Time Myoelectric Control of Multifunctional Artificial Arms. JAMA 301(6):619-628, 2009.

Short Biography Todd A. Kuiken received a B.S. degree in biomedical engineering from Duke University, a Ph.D. in

biomedical engineering from Northwestern University in Evanston, Illinois and his M.D. from

Northwestern University Medical School (1990). He is a board certified physiatrist. He is now the

Director of the Center for Bionic Medicine at the Rehabilitation Institute of Chicago and a Professor in

the Depts. of PM&R, BME and Surgery at Northwestern University. Dr. Kuiken is an internationally

respected leader in the care of people with limb loss: both as an active treating physician and as a

research scientist.


HAPTICS & DEXTERITY Thursday, September 11, 2014

09h00 – 09h40 Roland Johansson (Umeå U) KEYNOTE Edge-orientation processing in first-order tactile neurons

09h40 – 10h00 Michael Dimitriou (Umeå U) D04T Human muscle spindles preferentially encode imposed movement

10h00 – 10h20 Ian Bullock (Yale U) D11T Kinematics of two- and three-fingered dexterous precision manipulation


10h50 – 11h20 Eric Rouiller (U Fribourg) KEYNOTE Behavioral variability of manual dexterity in macaques

11h20 – 12h20 Poster session (Sala Balint)

12h30 – 14h30 Lunch

14h30 – 15h10 Francisco Valero-Cuevas (U Southern California) KEYNOTE Moving beyond a cortico-centric view of dexterity

15h10 – 15h50 Aaron Dollar & Thomas Feix (Yale U) D10T Modeling of precision grip in primates

15h50 – 16h20 Coffe

16h20 – 17h00 Vincent Hayward (UPMC Paris) KEYNOTE Mechanics of the fingertip and its impact on the prehensile and sensory function of the hand

17h00 – 17h20 Sarah Wohlman (Northwestern U, RIC) D06T Subject variability during maximum lateral pinch

17h20 – 17h40 Andreas Thomik (Imperial College London) D07T Symbolic representation of complex action sequences



20h30 – 21h30 Social Event: Dimitri & Compagnia Due (Sala Balint)

TA

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(T

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KEYNOTE

Edge-orientation processing in first-order tactile neurons

R. S. Johansson1*, J. A. Pruszynski

1

1 Physiology Section, Dept. of Integrative Medical Biology, Umeå University, Umeå, Sweden

Abstract A fundamental feature of first-order neurons in the tactile system is that their distal axon branches in

the skin and forms many transduction sites, yielding complex cutaneous receptive fields with many

highly sensitive zones [1,2]. The functional consequences of this spatial arrangement are unknown.

Here we demonstrate that this arrangement constitutes a peripheral neural mechanism that allows

individual neurons to signal geometric features of touched objects. Specifically, we show that two

types of first-order tactile neurons that densely innervate the glabrous skin of the human fingertips

signal edge orientation via both the intensity and the temporal structure of their responses. Moreover,

a neuron’s sensitivity to edge orientation can be predicted from the spatial layout of its highly sensitive

zones. We submit that peripheral neurons in the touch-processing pathway, like peripheral neurons in

the visual-processing pathway, perform feature extraction computations typically attributed to neurons

in the cerebral cortex.

References [1] Johansson, R.S. (1978) Tactile sensibility in the human hand: receptive field characteristics of

mechanoreceptive units in the glabrous skin area. J. Physiol. 281, 101-125

[2] Phillips, J.R., Johansson, R.S. and Johnson, K.O. (1992). Responses of human mechanoreceptive afferents to embossed dot arrays scanned across fingerpad skin. J. Neurosci. 12, 827-839

Short Biography Roland S. Johansson is since 1988 Professor of Physiology at Umea University and since 2004 a

member of The Royal Swedish Academy of Sciences. His primary research interests are in the

organization and operation of neural sensory and motor mechanisms that endow human hands with

their extraordinary ability to manipulate physical objects and tools. His work is cited >15 000 times and

results are represented in several standard textbooks in Neuroscience and in Physiology.


D04T

Human muscle spindles preferentially encode imposed movement

M. Dimitriou1*

1 Physiology Section, Department of Integrative Medical Biology, Umeå University, Sweden

Abstract Muscle spindles are commonly considered as stretch receptors encoding movement, but the

functional consequence of their motor control has remained unclear. Of the several hypotheses on

spindle and fusimotor function, the α-γ co-activation hypothesis1 states that activity in a spindle-

bearing muscle is positively related to its spindle afferent responses. However, given reciprocal

inhibition, activity in one muscle should also be negatively related with spindle afferent activity from its

antagonist. Taken together, the above suggest that spindle afferent responses should be affected by

agonist/antagonist activation balance. Here, I show that spindle afferent firing is indeed affected by

agonist/antagonist balance, in addition to muscle stretch. Specifically, spindle afferent activity from the

common finger extensor muscle was recorded while alert human subjects constantly moved a single

finger under external bias loads that either resisted or assisted finger flexion. Regardless of identical

movement profiles across load conditions, stretch of the loaded antagonist muscle (i.e., extensor) was

accompanied by increased spindle afferent firing from this muscle compared to a baseline case of no

load. In contrast, spindle firing rates from the stretched extensor were lower than baseline when the

agonist muscle powering movement (i.e., flexor) acted against an additional resistive load. Stepwise

regressions confirmed that angular velocity, extensor and flexor muscle activity had a significant

impact on spindle afferent responses, with flexor activity having a negative effect. The results

therefore indicate that, as a consequence of their fusimotor control and basic spinal circuitry, spindle

encoding of effortful self-generated motion is attenuated whereas externally imposed movement is

preferentially encoded. In addition to offering a direct measure of movement ‘exafference’, such

spindle signals can also allow reflexive fine-tuning of reciprocal inhibition during movement.

References [1] Vallbo, A.B. (1970). Discharge patterns in human muscle spindle afferents during isometric voluntary

contractions. Acta Physiologica Scandinavica. 80(4):552-66

Short Biography Michael Dimitriou received his PhD degree from Umeå University (2009). His thesis work

involved recording and characterizing the responses of sensory afferents from human muscles using

the technique of microneurography. Between 2010 and 2012, he was a postdoc at the laboratory

of Prof. Daniel Wolpert (Department of Engineering, University of Cambridge) looking into the task-

dependency of reflex responses. Michael currently works at the Department of Integrative Medical

Biology of Umeå University, and his research includes investigating how the nervous system itself

shapes sensory output.


Pointed object used in the study, and a 3D view of the three-fingered workspace obtained from one participant.

D11T

Kinematics of two- and three-fingered dexterous precision manipulation

I. M. Bullock*1, T. Feix

1, A. M. Dollar

1

1 Department of Mechanical Engineering & Materials Science, Yale University

Abstract Precision manipulation, in which an object

held between the fingertips is translated

and/or rotated with respect to the hand

without sliding, is used frequently in

everyday tasks such as writing, yet few

studies have examined the experimental

precision manipulation workspace of the

human hand. This study evaluates the range

of positions over which 19 participants

manipulated a moderately sized (3.3-4.1cm

diameter) object using either the thumb and

index finger (2 finger condition) or the

thumb, index and middle fingers (3 finger

condition). The results show that the 2-

fingered workspace is on average 40 %

larger than the 3-fingered workspace

(p<0.001), likely due to added kinematic

constraints from an additional finger.

Representative precision manipulation

workspaces for a median 17.5cm length hand are analyzed to clearly illustrate the overall workspace

shape, while the general relationship between hand length and workspace volume is evaluated. This

view of the human precision manipulation workspace has various applications, ranging from

motivating the design of effective, comfortable haptic interfaces to benchmarking the performance of

robotic and prosthetic hands.

References [1] Bullock, I.M., Feix, T., and Dollar, A.M. (2014). Dexterous Workspace of Human Two- and Three-Fingered

Precision Manipulation. IEEE Haptics Symposium, 41-47.

[2] Bullock, I.M., Feix, T., and Dollar, A.M.. Analyzing Human Fingertip Usage in Dexterous Precision Manipulation: Implications for Robotic Finger Design. In review.

Short Biography Ian M. Bullock is a PhD candidate studying human grasping and dexterous manipulation at Yale

University. He earned a B.S. in Engineering from Harvey Mudd College (Claremont, CA) and an M.S.

and M.Phil. from Yale University. His research looks at the capabilities of the human hand from the

perspective of trying to improve robotic hand design, haptic interfaces, and rehabilitation efforts.


KEYNOTE

Behavioral variability of manual dexterity in macaques

E. M. Rouiller1*

1 Department of Medicine, University of Fribourg, Switzerland

Abstract The aim of the present study was to investigate the behavioral variability of manual dexterity in non-

human primates (macaque monkeys: macaca fascicularis) in order to assess whether the variability

during learning and consolidation phases is a predictor of the extent of functional recovery following a

lesion of the motor cortex and of the post-lesion variability. Moreover, the possible impact of hand

dominance as well as hand preference was examined. Hand preference was quite variable across

individuals and was task dependent. Hand dominance did not show a systematic lateralization at

group level, but there was a tendency at individual level to show hand dominance, for the right hand in

some animals and for the left hand in other animals. A high level of manual dexterity performance at

consolidation phase pre-lesion is predicted by an also high initial score before learning, but not by the

initial variability at the beginning of the training. Motor habit, corresponding to the temporal order of

free-will sequential grasping movements was established very early during the learning phase. The

learning phase resulted in an optimization of manual dexterity attributes, such as score, contact time,

and a decrease of intra-individual variability. Following unilateral lesion of the motor cortex, the

duration of the incomplete recovery of manual dexterity of the affected hand to reach a post-lesion

plateau was correlated with the volume of the lesion in the gray matter. This was also true for the post-

lesion variability of the recovered manual dexterity. The pre-lesion variability of manual dexterity,

either at the beginning of the learning phase or at plateau, is not a predictor of the variability post-

lesion. Overall, the data emphasize the considerable inter-individual variability of manual dexterity in

non-human primates, to be considered for further pre-clinical applications based on this animal model.

Short Biography The author was trained as Biologist at the University of Lausanne (Switzerland), followed by a Ph.D.

degree in Neurophysiology in the field of hearing (1981), a topic in which he spent then 2 years as

post-doc at Harvard Medical School (1981-1983; Prof. N. Kiang’s laboratory). After a second post-doc

at University of Lausanne, a position of Junior Professor was occupied at the University of Fribourg

(1989-1996) in the field of motor control on monkeys (laboratory of Prof. M. Wiesendanger), followed

by a promotion as associate Professor (1996) and as full Professor (2003).


KEYNOTE

Moving beyond a cortico-centric view of dexterity

F. J. Valero-Cuevas1*

1 Department of Biomedical Engineering, and Division of Biokinesiology & Physical Therapy University of

Southern California, Los Angeles, CA, USA

Abstract The literature in support of cortical involvement in dexterous manipulation is large. Our own fMRI

studies [2] agree with many others showing differential activity across cortical networks depending on

the level of difficulty of an unstable manipulation task. We have also proposed that competition

between descending commands for manipulation likely involves the phylogenetically older

reticulospinal and the newer corticospinal tracts [3]. But recent results [1] compel us to confront

several inconvenient facts to the cortico-centric view of the neural control of the hand including time

delays, our evolutionary history, and clinical symptomatology. For example, dynamic manipulation of

unstable objects with the fingers and legs occur at time scales for which spino-cortico-spinal delays

would compromise closed-loop control. These issues can be resolved by paying more attention—and

due credit—to subcortical mechanisms. That is, neural control must involve sub-cortical and spinal-

mediated control, as proposed by hierarchical, or at the very least distributed, perspectives on neural

control. In fact, neuroanatomists and electrophysiologists since the time of Sherrington have sought to

map the circuitry in the spinal cord to understand the spinally-mediated excitation-inhibition

mechanisms that enable voluntary function—and produce the clinical symptomatology of, for example,

dystonia in some neurological disorders including stroke, cerebral palsy, and spinal cord injury. These

results compel future work to disambiguate among peripheral, spinal and cortical contributions to, and

mechanisms for, finger and limb dexterity.

References [1] Lawrence EL, Fassola I, Werner I, Leclercq C, Valero-Cuevas FJ. Quantification of dexterity as the dynamical

regulation of instabilities: comparisons across gender, age, and disease. Frontiers in Neurology - Movement Disorders, 5:53. doi: 10.3389/fneur.2014.00053, 2014.

[2] Mosier K, Lau C, Wang Y, Venkadesan M, and Valero-Cuevas FJ. Controlling instabilities in manipulation requires specific cortical-striatal-cerebellar networks. Journal of Neurophysiology, 105: p.1295–305, 2011.

[3] Rácz K, Brown D, and Valero-Cuevas FJ. An involuntary stereotypical grasp tendency pervades voluntary dynamic multifinger manipulation. Journal of Neurophysiology, 108: p.2896-911, 2012.

Short Biography Prof. Valero-Cuevas earned a BS in Engineering from Swarthmore College (USA), was a Thomas J

Watson Fellow in the Indian subcontinent, and completed an MS and PhD in Mechanical Engineering,

respectively, at Queen's University (Canada) and Stanford University (USA). He was Research

Associate and Lecturer at Stanford University, and assistant and tenured associate professor at

Cornell University. He is full professor in the Department of Biomedical Engineering, and the Division

of Biokinesiology & Physical Therapy at the University of Southern California. He is a Senior Member

of the IEEE, and Fellow of the American Institute for Medical and Biological Engineers.


D10T

Modeling of precision grip in primates

T. Feix1*, A. M. Dollar

1


Abstract For decades humans were considered to be the only primate

species capable of dexterous precision gripping. However,

recent behavioral studies have shown that other non-human

primates, particularly extant great apes (hominoids) and some

Old World and New World monkeys are also capable of

precision gripping. We present a novel method based on

robotic workspace modeling that allows us to estimate the

precision grip capabilities between the thumb and index finger

across a broad sample of primates. Although this kinematic

model simplifies the complexity of morphology and movement

typical of the primate hand, this simplicity enables the model

to be applied to a wide range of associated fossil specimens

in which knowledge about joint movements, soft tissue

morphology, or other bones (e.g. carpals) are unknown. This

model offers for the first time a method of assessing

movement within the hand, rather than simply inferring

function from the bony morphology (in the case of fossils).

We use the model to analyze the precision gripping behavior of 360 hand specimens from 38 different

primate species. Within the hominoid group, the workspace of the human is the largest, implying that

the human hand is best adapted for precision gripping. However, the results show that other non-

hominoid species are able to achieve workspaces that are similar to the human, supporting the view

that hands of other species are also capable of precision gripping. The model also shows that a longer

thumb is a good predictor of thumb-index gripping potential.

References [1] Feix, T., Kivell, T.L., Pouydebat, E., and Dollar, A.M. (2014). Estimating precision grip potential in extant

Hominoids. In preparation

Short Biography Thomas Feix received the M.Sc. degree in sports equipment technology from the University of Applied

Sciences Technikum Wien, Vienna, Austria, and the Ph.D. degree from the Vienna University of

Technology, in 2011. He is a Postdoctoral Associate with the GRAB Lab, Department of Mechanical

Engineering, Yale University, New Haven, CT. His research is focused on human grasping and

manipulation and its application to robotics and prosthetics.


KEYNOTE

Mechanics of the fingertip and its impact on the prehensile and sensory function of the hand

V. Hayward1*

1 Sorbonne Universités, UPMC Univ Paris 06, UMR 7222, ISIR, F-75005, Paris, France

Abstract Recent studies pertaining to the detailed mechanics of the human fingertip have revealed a number of

interesting and surprising properties of this organ. It is believed that these mechanical and tribological

properties play a major role in the hand extremities' prehensile and sensory capabilities. These

observations will be discussed in relation to the scientific study of touch and of its applications in the

design of haptic interfaces.

References [1] Hayward, V. (2011). Is There a ‘Plenhaptic’ Function? Philosophical Transactions of the Royal Society B,

366:3115–3122

[2] Hayward, V., Terekhov, A. V., Wong, S.-C., Geborek, P., Bengtsson, F., J rntell, H. (2014). Spatio-Temporal Skin Strain Distributions Evoke Low Variability Spike Responses In Cuneate Neurons. Journal of the Royal Society Interface, 11(93):20131015

Short Biography Vincent Hayward joined CNRS, France, as Chargé de Recherches in 1983. In 1987, he joined the

Department of Electrical and Computer Engineering at McGill University as assistant, associate and

then full professor (2006). He was the Director of the McGill Center for Intelligent Machines from 2001

to 2004. Since 2008 is a Professeur at the Université Pierre et Marie Curie, Paris, France. Hayward is

interested in haptic device design, haptic perception, and robotics. He is on editorial board of the ACM

Transaction on Applied Perception and of the IEEE Transactions on Haptics and is a Fellow of the

IEEE.


D06T

Subject variability during maximum lateral pinch

S. J. Wohlman1,2

*, M. de Bruin1,2

, W. M. Murray1,2,

1 Northwestern University, Department of Biomedical Engineering, Evanston, IL, USA

2 Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA

Abstract Recording experimental data from the thumb during lateral pinch is difficult. A highly variable range of

forces and muscle activations have been observed across subjects [1]. The source of this variability is

not clear. Here, we simultaneously record muscle activation, joint posture, and thumbtip force to

analyze such variability during maximum lateral pinch.

Muscle activations were recorded via intramuscular EMG in the four extrinsic thumb muscles. Using a

27 gauge hypodermic needle, bipolar fine-wire electrodes were inserted in each muscle. Data were

collected with a Delsys Bagnoli-16 system at 2000 Hz and filtered. Thumbtip forces were collected

with a Biometrics Precision Pinchmeter P100, and joint posture was quantified using a Cyberglove.

Subjects were given visual feedback and instructed to generate maximum force. Data were smoothed

via a 750 ms moving average and maximum force was identified. Muscle activations and joint angles

were calculated by averaging over the same 750 ms window as the maximum force. EMG data were

normalized by each muscle’s activation, quantified individually during maximum voluntary contraction

testing.

Maximum lateral pinch force averaged 83 ± 17N across four subjects. In three male subjects, EPL,

EPB, and APL were at most 25% of FPL activation (Fig. 1A bottom panel). In the female subject,

muscle activations for these three muscles were 43% of FPL activation, on average (Fig. 1B bottom

panel). This subject had substantial

MCP joint extension with concurrent IP

joint flexion (Fig. 1B). The male

subjects adopted a less extreme

posture (Fig. 1A).

We simultaneously recorded muscle

activations, joint angles, and thumbtip

forces while subjects produced

maximum lateral pinch. A single female

subject differed in muscle activation

pattern and thumb posture from three

male subjects. The posture she

adopted is consistent with higher joint

laxity, a proposed precursor to thumb

osteoarthritis, a pathology more

prevalent in women.

References [1] Valero-Cuevas, F.J., Johanson, M.E., and Towles, J.D. (2003). Towards a realistic biomechanical model of the

thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty of musculoskeletal parameters. Journal of Biomechanics, 36(7): 1019-30.

[2] Buffi, J.H., Sancho Bru, J.L., Crisco, J.J., and Murray, W.M. (in review). Evaluation of hand motion capture protocol using computed tomography: application to an instrumented glove. Journal of Biomechanical Engineering.

Short Biography Sarah is a PhD candidate in the Department of Biomedical Engineering at Northwestern University.

She received her MS from Northwestern in 2012. Her research focuses on thumb biomechanics.

Fig 1. Force, posture, and activation data ± standard deviation for A) N = 3 males and B) N = 1 female.


D07T

Symbolic representation of complex action sequences

A. Thomik1*, A. A. Faisal

1,2,3

1 Dept. of Bioengineering, Imperial College London

2 Dept. of Computing, Imperial College London

3 MRC Clinical Sciences Centre, Imperial College London

Abstract A fundamental problem in neuroscience is to understand how the brain translates a symbolic

sequence of action descriptors, or high-level motor intention, into the appropriate muscle commands.

This is of particular interest in the context of brain-machine interfaces and neuroprosthetics, where

attempts to achieve functionality comparable to natural limbs have failed so far. This is not a

mechatronic limitation but the robotic control and coordination of so many degrees of freedom (21 in

the hand alone) is beyond the capabilities of current computers and algorithms. We take the view that

the brain achieves this feat by mapping the necessary computation onto a finite and low-dimensional

subset of control building blocks of movement, characterised by high correlation between a subset of

the joints involved – kinematic primitives.

To investigate this possibility, we collected a data set of annotated hand movement from subjects

wearing lightweight and unobtrusive data gloves while going about their daily life. We process this

data by applying a method which extracts kinematic primitives by analysing the local correlation

structure of the data. This yields a dictionary of kinematic primitives which could be used by the brain

to generate the necessary motor commands. Crucially, this technique also allows computation of the

reverse problem by asking at any point in time which primitive was most likely to have produced the

data observed. In this way, we can translate the time-series of joint movements into a symbolic

sequence (“behavioural barcode”), compressing the very complex movement necessary to achieve a

given task into a sequence of actions. From a scientific perspective, this step from raw movement to

behavioural grammar may give us some insight into the neural computations underlying our motor

system, while from a technical point of view this compression may provide us with a more

sophisticated way of controlling robotic limbs or prostheses.

Short Biography Andreas Thomik received his B.Sc. in Microengineering from EPFL in 2010, followed by a M.Sc. in

Biomedical Engineering from Imperial College London in 2011 where he stayed to pursue a Ph.D. in

the field of Neurotechnology at the Brain & Behaviour Lab. His research interests range from the

neuroscience of sensorimotor control to applications in prosthetics and assistive devices. The main

focus of his PhD thesis is the hierarchical structure and grammar of human behaviour, for which he

has developed computational and experimental techniques.


NEURO-

REHABILITATION Friday, September 12, 2014

09h00 – 09h40 Joachim Hermsdörfer (TU Munich) KEYNOTE Deficits of tool use following stroke: Neural correlates and technological approaches to assist in activities of daily living

09h40 – 10h00 Ted Milner (McGill U, CRIR Montreal) E02T Coordination of grip force and load force during submovements in normal and post-stroke subjects

10h00 – 10h20 Margaret Duff (RIC) E03 A portable, low-cost system for evaluating hand function during natural movement


10h50 – 11h20 Derek Kamper (Illinois Institute of Technology, RIC) KEYNOTE Neurological interactions among thumb and fingers

11h20 – 11h40 Alejandro Melendez-Calderon (Hocoma, Northwestern U) E06T Assistance and rehabilitation of hand function using a robotic glove

11h40 – 12h00 Arno Stienen (U Twente) E09T Symbionic hand orthoses for Duchenne and stroke

12h00 – 12h20 CSF Junior Award ceremony & closing words

12h30 – 14h30 Lunch

TA

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EU

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KEYNOTE

Deficits of tool use following stroke: Neural correlates and technological approaches to assist in activities of daily living

J. Hermsdörfer1*

1 Department of Movement Science, Faculty of Sport and Health Science, Technische Universität München,

Munich, Germany

Abstract Stroke frequently causes apraxia, particularly if it affects the left-hemisphere. A frequent symptom of

apraxia is impaired use of tools performed as a pantomime as well as during actual use. We studied

the neural correlates of tool use in healthy subjects using functional magnetic resonance imaging

(fMRI) and a tool-carousel that allowed us to investigate the actual use of tools. We compared actual

tools use with goal-directed movements of a neutral object that did not encompass knowledge about

use. We also compared use with a simple transport movement. We found a left lateralized brain

network responsible for planning and execution of the task with a stronger leftward shift the more the

task involved actual use of a tool compared to a neutral object and simple transport. In addition, we

found characteristic brain areas active that could be related to the dorso-dorsal stream for online grasp

control, to the ventro-dorsal stream for tool manipulation, and the ventral stream for object recognition

and tool semantics.

Damage to the neural substrates controlling tool use may substantially limit the capacity of stroke

patients to live independently in their home environment. The second part of the presentation

introduces the CogWatch project that provides technology based means of assistance and

rehabilitation for patients with ADL impairments due to tool use and action organization deficits. The

system bases on instrumented objects and ambient devices that are part of patients' everyday

environment and can be used to monitor behavior and progress as well as re-train them to carry out

ADL through persistent multimodal feedback.

References [1] Hermsdörfer J, Terlinden G, Mühlau M, Goldenberg G, Wohlschläger a M (2007). Neural representations of

pantomimed and actual tool use: evidence from an event-related fMRI study. Neuroimage 36 Suppl 2: 109–118.

[2] Hermsdörfer J., Bienkiewicz, M., Cogollor, J., Russel, M., Jean-Baptiste, E., Parekh, M., Wing, A., Ferre, M., Hughes, C. (2013). "CogWatch - Automated assistance and rehabilitation of stroke-induced action disorders in the home environment." Lecture Notes in Computer Science 8020 LNAI (PART 2): 343-350.

Short Biography Joachim Hermsdörfer received his PhD at the Institute for Medical Psychology in the Ludwig-

Maximilians-University in Munich in 1993. He headed the research group “Sensorimotor Disturbances”

at the Clinical Neuropsychology Research Group in the Hospital München-Bogenhausen. In 2010 he

was appointed as Full Professor and Chair of Movement Science in the Faculty of Sports and Health

Sciences at the Technische Universität München. His main interest is sensorimotor-control in healthy

individuals and in patients with neurological diseases using behavioral as well as neuroimaging

approaches.


E02T

Coordination of grip force and load force during submovements in normal and post-stroke subjects

T. Milner1,3

*, H. Kazemi2,3

1 Department of Kinesiology and Physical Education, McGill University

2 Department of Biomedical Engineering, McGill University

3 Centre de recherche interdisciplinaire en réadaptation du Montréal métropolitain

Abstract A feature of twisting movements against a spring load is that grip force increases linearly with load

force in a tightly coordinated manner. This tight coordination is frequently absent following stroke [1].

However, movements in post-stroke (PS) subjects are characterized by a series of submovements

similar to movements performed at slower than normal speed by neurologically normal (NN) subjects.

We investigated whether grip force (GF) and load force (LF) were tightly coupled in NN subjects

during slow movements where submovements were present and compared the results when PS

subjects performed the same movements at normal speed with their contralesional hand. For NN

subjects, GF increased linearly (r>0.9) with LF during more than 50% of submovements made with the

dominant (right) hand. However, when movements were made with the non-dominant (left) hand we

found r>0.9 for less than 25% of the submovements. For a small number of submovements, (6% right,

12% left) GF decreased linearly as LF increased (i.e., r<0.9).

In the case of PS subjects, the overall distribution of correlation coefficients was more similar to that of

the non-dominant than the dominant hand of age-matched NN subjects. PS subjects appeared to fall

into two broad categories. In one category, subjects modulated GF linearly with LF, although unlike

NN subjects, they tended to increase or decrease GF with almost equal frequency as LF increased.

The correlation coefficients had a bimodal distribution where the majority fell in the ranges 1<r<0.8

and 0.8<r<1. Even subjects who appeared to have completely recovered upper limb function following

the stroke (perfect scores of 63 on the 9-component CAHAI) showed this abnormal tendency to

reduce GF as LF increased during submovements. In the other category, modulation of GF was quite

variable during submovements such that the distribution of correlation coefficients was relatively

uniform.

References [1] Kazemi, H., Kearney, R.E. and Milner, T. (2013) Characterizing coordination of grasp and twist in hand

function of healthy and post-stroke subjects. ICORR 2013.

Short Biography Ted Milner is a professor in the Department of Kinesiology and Physical Education at McGill

University. He is currently visiting professor and Marie Curie Fellow at ETH-Zurich. He and his former

research trainees, Etienne Burdet and David Franklin have recently written a book titled Human

Robotics Neuromechanics and Motor Control. He has been investigating the control of upper limb

movements for the past 30 years with a recent focus on rehabilitation of hand function after stroke. He

is currently investigating how the brain processes and transforms somatosensory information in the

control of dexterous hand movements.


E03T

A portable, low-cost system for evaluating hand function during natural movement

M. Duff1*, J. Hudson

1, W. Z. Rymer

1

1 Rehabilitation Institute of Chicago, Chicago, IL

Abstract Tangible interaction with external objects in one’s environment is crucial to participating in many

activities of daily living, yet this capacity can be severely compromised after a stroke1. Currently there

are few ways to consistently and comprehensively measure hand function during natural movement,

which makes providing real-time feedback during repetitive task therapy difficult. A low-cost and

portable system was developed for recording and analyzing key kinematic and kinetic features of

reaching, grasping and object transportation tasks after stroke, as a means to rapidly and

economically quantify hand impairment. The system is comprised of one depth sensor motion capture

camera that provides non-contact tracking of hand and finger movements. This camera provides

spatial resolution comparable to marker-based optical camera capture, with slightly reduced temporal

resolution of approximately 30 frames per second. The system also incorporates custom objects that

mimic objects used in activities of daily living and can wirelessly sense touch, applied force and object

acceleration and orientation. Initial data collected from unimpaired participants is being used to

develop quantitative measures of hand use and function during reaching tasks. The raw data is

transformed into useful metrics such as: hand speed, hand trajectory, wrist rotation, hand aperture,

grasp force and object movement and orientation. These metrics will be the basis of an evaluation

framework of hand impairment after stroke, which will drive engaging and informative audio and visual

feedback during therapy. Data is collected and analyzed through a web-based software interface,

creating a suitable structure for home-based tele-rehabilitation systems and monitoring of how therapy

translates to hand use in everyday life.

References [1] Raghavan, P. (2007). The nature of hand motor impairment after stroke and its treatment. Current Treatment

Options in Cardiovascular Medicine, 9: 221-228.

Short Biography Margaret Duff received her PhD in biomedical engineering from Arizona State University where she

developed and evaluated mixed reality rehabilitation systems for use after stroke. She is currently a

post-doctoral fellow at the Rehabilitation Institute of Chicago with Dr. Zev Rymer. Her research

includes computational modeling of motor learning through kinematic measurements, replicating

clinician decision making through machine-learning algorithms and developing inexpensive and easy

to use hardware and software infrastructures for unsupervised interactive therapy.


KEYNOTE

Neurological interactions among thumb and fingers

D. Kamper1,2

*

1 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616

2 Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611

Abstract Coordinated movement of the thumb and fingers is one of the hallmarks of human motor control. The

flexibility of these patterns is striking in that thumb and finger movements can be highly independent

when typing or playing an instrument, yet highly coupled when grasping an object. The range of

possible interactions is fundamental to dexterous hand manipulation in humans, yet questions

regarding these capabilities remain. We have been exploring these issues in a series of studies,

ranging from reflexive to voluntary to learning paradigms. Using a novel finger exoskeleton, we were

able to examine reflexive coupling between index finger and thumb muscles. Stretch of either the

index finger flexors or extensors was applied during performance of voluntary pinching movements

with the index finger and thumb. Reflex responses of similar latency were seen in both the stretched

muscles of the index finger and the non-stretched muscles of the thumb. Another experiment

employed the same device to arrest movement of specific index finger joints during voluntary pinching.

During pinch closing, impedance of index finger movement led to corresponding reductions in thumb

movement. In another study, we assessed transfer of motor adaptation from the index finger to the

thumb and vice versa. Preliminary analysis suggests that learning of a novel motor task with one digit

– in this case involving movement of a haptic object with novel dynamic properties – led to faster

adaptation with the other digit despite a difference in required digit kinematics. Yet, a functional

magnetic resonance imaging study we conducted revealed distinctive cortical regions for thumb and

finger activation, especially for the dominant hand. Additionally, EMG-EMG coherence between thumb

and finger muscles can be quite small for cortically derived signals. Thus, non-cortical pathways, such

as from brainstem [1,2], may play important roles in coordinating finger and thumb movement.

References [1] Honeycutt, C.F., Kharouta, M., Perreault, E.J. (2013). Evidence for reticulospinal contributions to coordinated

finger movements in humans, J Neurophysiol, 110(7): 1476-83.

[2] Rácz, K., Brown, D., Valero-Cuevas, F. (2012). An involuntary stereotypical grasp tendency pervades voluntary dynamic multifinger manipulation. J Neurophysiol 2012; 108(11): 2896-911.

Short Biography Dr. Derek Kamper received a B.E. in Electrical Engineering from Dartmouth College and M.S. and

Ph.D. degrees in Biomedical Engineering from Ohio State University. He is currently an Associate

Professor in the Department of Biomedical Engineering at the Illinois Institute of Technology and a

Research Scientist at the Rehabilitation Institute of Chicago. His research focuses on hand

neuromechanics and rehabilitation of hand motor control following neurological injury.


E06T

Assistance and rehabilitation of hand function using a robotic glove

A. Melendez-Calderon1,2

*, A. Duschau-Wicke1, G. B. Prange

3, A. I. R. Kottink

3, J. Ingvast

4

1 Hocoma AG, Switzerland

2 Department of Physical Medicine & Rehabilitation, Northwestern University, USA

3 Roessingh Research and Development, Netherlands

4 Bioservo Technologies AB, Sweden

Abstract Good hand function is paramount to the performance of almost all tasks in everyday life. After a

neurological disorder (e.g. stroke) or age-related conditions (e.g. sarcopenia, rheumatoid arthritis), our

capability to manipulate, explore and acquire objects, or even to communicate with the hands is lost or

limited. The HandinMind [1] and IronHand [2] projects aim at minimizing the loss of quality of life after

hand impairments by facilitating active execution of hand movements during everyday activities or

therapeutic practice. HandinMind focuses on the needs of stroke patients for activities of daily life,

while IronHand focuses on supporting work and leisure activities of ageing individuals with various

limitations, such as weak grip or arthritis. Our approach is based on a grip enforcing robotic glove [3]

with added hand-opening functionalities for the stroke population and specific intention detection for

each target population. The glove can be used in two different modalities: i) assistive, in which the

glove is worn as any other glove and supports active execution of hand opening and closing during

every day or work activities and ii) therapeutic, in which the glove is connected to an external

computer and facilitates therapeutic practice at home or small clinic. In this session we will present

advances of the HandinMind and IronHand projects; in particular: i) mechanisms to provide extra force

for opening and closing the hand, ii) advances in an “intention detection” logic that activates the

support if and only if the user initiates the movement, depending on specific needs of either stroke or

elderly, and iii) therapeutic exercises and assessment of hand function using our robotic glove tailored

to either stroke or elderly population.

References [1] HandinMind – Robotic Glove for the rehabilitation of hand impaired patients (www.handinmind.eu). Project co-

funded by EUROSTARS (project number E!8227), State Secretariat for Education, Research and Innovation (SERI, Switzerland), and Swedish Governmental Agency for Innovation Systems (Vinnova, Sweden)

[2] IronHand - Smart glove with intention detection and mechatronic finger actuation supporting elderly occupation (www.ironhand.eu). Project co-funded by Ambient Assisted Living (European Union), State Secretariat for Education, Research and Innovation (SERI, Switzerland), Swedish Governmental Agency for Innovation Systems (Vinnova, Sweden) and the Netherlands Organisation for Health Research and Development (ZonMW).

[3] Nilsson, M., Ingvast, J., Wikander, J., & von Holst, H. The Soft Extra Muscle system for improving the grasping capability in neurological rehabilitation. IEEE EMBS, 2012.

Short Biography Alejandro Melendez-Calderon received his doctoral degree from Imperial College London for research

at the interface of robotics, rehabilitation and human motor control. After his studies, he joined the

Rehabilitation Institute of Chicago and Northwestern University as postdoctoral research fellow. Since

January 2014, he works at Hocoma AG as Technical Project Manager. He is also an Adjunct Assistant

Professor at Northwestern University.

http://www.handinmind.eu/

http://www.ironhand.eu/


E09T

Symbionic hand orthoses for Duchenne and stroke

A. Stienen*1, D. Plettenburg

2, A. Bergsma

1, B. Koopman

1

1 Biomechanical Engineering, University of Twente, Enschede, NL

2 Biomechanical Engineering, Delft University of Technology, Delft, NL

Abstract Ever more people rely for their independence on wearable assistive devices that improve functional

capabilities. Traditionally, these assistive devices are adapted to the patient upon delivery but remain

static during their lifetime. In our view, devices should continuously adapt to the user according to a

therapy plan or to compensate for user changes, changing environment, or changing tasks. The

objective of the Symbionics program (STW, NL) is to create systems that co-adapt automatically,

either intrinsically by design, by control, or their combination. Furthermore, we aim to create assistive

devices that completely fit underneath regular clothing, which is key to social acceptance.

Human hands are versatile organs that can manipulate a wide range of objects in our environment.

The complexity of control over these prehensile, multi-fingered extremities is illustrated by the large

size of the sensorimotor cortex our brain has reserved for it. In the Symbionics program, two projects

focus on the development of hand orthoses for individuals after stroke or those suffering from

Duchenne Muscle Dystrophy. We will develop algorithms that can be used in combination with

advance hand exoskeletons to simulate assistive performance of proposed hand orthoses. We will

also develop algorithms that can detect user intention despite the presence of debilitating movement

impairments. The results of these will be used to develop hand orthoses that focus on robot

rehabilitation after stroke and on assistance in activities of daily living for DMD patients.

It is especially important to balance the crossover from function recovery (rehabilitation) to

compensation (permanent assistance). Too much assistance will result in undesired disuse or

accelerated degeneration in the individual. On the other hand, too little assistance will cause errors in

movement execution. In other words, recovery and retention of function requires as little assistance as

possible to maximize the involvement of the user, whereas compensation requires that performance

errors be minimized for successful use with activities of daily living. This balance needs to be adjusted

for each joint in the human hand at each stage of the impairment.

Short Biography Arno Stienen acquired a PhD in biomechatronic engineering and rehabilitation robotics under the

guidance of prof. Frans van der Helm and prof. Herman van der Kooij. Currently, he is an Assistant

Professor at the University of Twente, specializing in upper extremity motor learning and rehabilitation.

He is also an Adjunct Assistant Professor at Northwestern University in Chicago (IL, USA), where he

works with prof. Jules Dewald. The Symbionics projects on the hand are supported by STW (NL,

#12479, #13524 and #13525), Hankamp Rehabilitation (Enschede, NL), Hocoma (Volketswil, CH),

TMSi (Oldenzaal, NL), Moog (Nieuw Vennep, NL), FESTO (Delft, NL), and multiple Duchenne

foundations (NL & USA).


POSTERS Section Page

CORTICAL CONTROL 61

COGNITIVE & CLINICAL NEUROSCIENCE 73

NEUROPROSTHETICS 82

HAPTICS & DEXTERITY 87

NEUROREHABILITATION 95


CORTICAL CONTROL

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A01P

Task difficulty modulates left inferior frontal cortex during matching of hand posture to object use

G. Vingerhoets1,2

*, J. Nys1,2

, P. Honoré2, E. Vandekerckhove

2, P. Vandemaele

2,3

1 Department of Experimental Psychology, Ghent University, Ghent, Belgium

2 Ghent Institute for Functional and Metabolic Imaging, Ghent University, Ghent, Belgium

3 Department of Radiology, Ghent University, Ghent, Belgium

Abstract The present study aimed to identify modulation in human brain areas sensitive to the difficulty level of

tool object - hand posture matching. We hypothesized that conditions where demands on

differentiation of hand posture and finger composition were higher, would show enhanced modulation

in the neural region responsible for hand posture selection, and that this region would most likely

correspond to the ventral premotor cortex [1].

Seventeen healthy right handed participants had to decide if a hand posture matched a tool object for

its functional use (experimental conditions) or if two images were identical (control condition) while

undergoing fMRI. Pairings could be within grasp types (difficult ‘Within grasp type’ decision) resulting

in a Match or Mismatch decision (the latter termed the Mismatch Hard condition). Alternatively,

pairings could be between grip types (easy ‘Between grasp type’ decision). In that case the pairing is

always a mismatch, and this condition was termed the Mismatch Easy condition.

The behavioral data revealed a successful manipulation of the mismatch conditions’ difficulty level.

Comparison of easy versus more difficult conditions was taken to reflect selective modulation in those

brain areas that would have to deal with this increased task demand.

In the Mismatch Hard > Mismatch Easy contrast, substantial response to task difficulty was elicited in

the left ventral premotor cortex, in particular in pars opercularis (BA 44) of the inferior frontal gyrus.

The same region was active in the more general Within > Between Grasp type choice contrast.

These findings are in agreement with other studies that targeted the hand posture selection process

and found vPMC activation among other activated regions. The merit of the present study is that it

highlights the selective response of this region to differing demands in the discrimination of hand

posture choice [2]. The selective involvement of vPMC in hand posture discrimination relative to object

properties remains in agreement with the functional role of primate F5 as proposed by Fagg & Arbib

[1], despite the increased complexity of transitive actions in humans.

References [1] Fagg, A.H., Arbib, M.A., 1998. Modeling parietal-premotor interactions in primate control of grasping. Neural

Networks 11, 1277-1303.

[2] Makuuchi, M., Someya, Y., Ogawa, S., Takayama, Y., 2012. Hand shape selection in pantomimed grasping: Interaction between the dorsal and the ventral visual streams and convergence on the ventral premotor area. Human Brain Mapping 33, 1821-1833.

Short Biography Guy Vingerhoets, PhD, was trained as a clinical neuropsychologist and worked for over 20 years with

patients suffering from a broad range of neurodegenerative diseases including Alzheimer’s disease,

Parkinson’s disease, epilepsy, and stroke. He also investigated cognitive sequelae in patients

following different types of cardiovascular surgery. In addition, he specialized in functional brain

imaging using transcranial Doppler ultrasonography and magnetic resonance imaging. Over the last 7

years his research has focused primarily on brain imaging, motor cognition, and hemispheric

specialization. Dr. Vingerhoets is Full Professor of Neuropsychology at the Department of

Experimental Psychology, Faculty of Psychology and Educational Sciences at Ghent University,

Belgium.


A02P

A unilateral lesion of the hand representation in the primary motor cortex in macaques affects the interhemispheric ratio of

SMI-32 stained neurons in premotor cortical areas

E. Schmidlin1*, R. Colangiulo

1, A. Contestabile

1, M. Kaeser

1, J. Savidan

1, A.-F. Wyss

1, A. Hamadjida

1,

E. M. Rouiller1

1 Dept. of Medicine, University of Fribourg, Switzerland

Abstract The primary motor cortex (M1) is interconnected with premotor areas, in both hemispheres, such as

the rostral part of the ventral premotor cortex (PMv-r) and the supplementary motor area (SMA). To

assess possible effects of a permanent unilateral lesion of M1 (hand area) on PMv-r and SMA, we

counted in these 2 areas in each hemisphere the number of long projecting neurons, specifically

immunostained with the marker SMI-32, targeting neurofilaments in layer V, in three subpopulations of

adult macaque monkeys: (i) two intact control animals (ii) three animals with a unilateral lesion of M1;

(iii) two animals with a similar M1 lesion but treated with an antibody directed against the neurite

growth inhibitor Nogo-A. Preliminary data show that the lesion in M1, affecting the cortico-cortical

interconnections between M1 and the premotor cortical areas, resulted in a marked inter-hemispheric

difference in the expression of the SMI-32 immunostaining in layer V in SMA and PMv-r, as observed

in all M1 lesioned animals. In contrast, there was no significant interhemispheric difference in the two

intact animals. In addition, in the majority of cases, the ipsilesional hemisphere showed a decreased

number of SMI-32 stained neurons, as compared to the contralesional hemisphere. We also observed

a direct correlation between the extent of the lesion and the amplitude of the interhemispheric

difference of SMI-32 positive neurons, both for SMA and PMv-r.

The role played in functional recovery from M1 lesion by premotor cortices (SMA, PMv-r) either in the

ispilateral or in the contralateral hemisphere is still not clear. Further investigations are needed to

determine if the observed differences in SMI-32 staining are due to indirect, remote changes of layer V

neurons’ phenotype due, or to a change of metabolism in SMA and PMv-r during the recovery phase

until a post-lesion plateau of performance is reached.

Short Biography Eric Schmidlin is actually involved in a research project in Non-human primates (NHP) at the

University of Fribourg in Switzerland, trying to understand the mechanism behind functional recovery

after permanent lesion of the hand representation in the primary motor cortex, using behavioral, and

histological investigations techniques.

He made his PhD in Prof Eric Rouiller’s lab in Fribourg in 2004 on the consequences of a spinal cord

hemisection at cervical level in NHP, and then moved to UCL in Roger Lemon’s Lab as a research

fellow between 2005 and 2006. He received an Ambizione grant from the SNF in 2009.


A03P

Hand usage shapes finger representations in the primary motor cortex

N. Ejaz1*, A. Reichenbach

1, P. Zatka-Haas

1, J. Diedrichsen

1

1 Institute of Cognitive Neuroscience, UCL, London

Abstract The organizational structure of the primary motor cortex (M1) is currently poorly understood.

Experiments involving single-neuron electrophysiology [1] and micro-stimulation across sites in M1 [2]

both suggest that co-activated finger movements rather than single fingers are represented in the

neural population code. Here we asked whether the representation of co-activated hand movements in

M1 follows an organizational principle.

Eight healthy participants were asked to perform isometric finger presses with the right hand while

undergoing an fMRI scan. Participants were instructed to produce all combinations of 1-, 2-, 3-, 4- and

5-finger presses, giving a total of 31 chords that together span the entire space of possible finger

movements. The resulting activity patterns for each chord were projected unto the reconstructed

cortical surface for each subject. We observed that while activation patterns in M1 for similar chords

were highly variable across subjects, the 465 Mahalanobis distances between all possible pairs of

activation patterns were remarkably stable (average inter-subject correlation r=0.77, p<<0.0001). This

finding strongly suggests the existence of an invariant organization principle of hand movement

representation in M1 across subjects. Furthermore, we found that the observed fMRI distances did not

result from the co-activation of muscles required to produce movement, but instead, were best

explained by how we use our hands in everyday life. Together, our results provide the first steps

towards uncovering organizational principles across healthy subjects and assessing the effects of

disease related changes.

References [1] Schieber M.H. and Hibbard L.S. (1993) How somatotopic is the motor cortex hand area? Science 261(5120):

489-492

[2] Gentner R and Classen J. (2006) Modular organization of finger movements by the human central nervous system. Neuron 52(4): 731-742

Short Biography Naveed Ejaz is a post-doctoral researcher at the Institute of Cognitive Neuroscience, UCL, and has a

PhD. in Systems Neuroscience from Imperial College London. He uses a combination of fMRI, EMG

and motion-capture equipment to study the representation and control of hand function in healthy and

diseased populations.


A04P

Fine manual dexterity is affected by transient inactivation of primary motor cortex (M1) using repetitive transcranial

magnetic stimulation (rTMS)

M. Kaeser1*, C. Roux

1, M. Fregosi

1, J. Savidan

1, M. Mouthon

2, L. Spierer

2, E. M. Rouiller

1,

E. Schmidlin1

1 Neurophysiology, Dpt of Medicine, University of Fribourg

2 Neurology, Dpt of Medicine, University of Fribourg and HFR Fribourg-Hospital

Abstract The aim of this study is to assess the role played by M1 in the execution of a behavioral task involving

a synergic action of proximal and distal muscles called the “reach and grasp” drawer task, before and

after transient inactivation of M1 using rTMS.

We analyzed several motor aspects: 1) the temporal unfolding of the task; 2) the continuous

recordings of the force needed to grasp the button of the drawer (grip force) and the force needed to

open the drawer against adjustable levels of resistance (load force); 3) the electromyographic (EMG)

activity of eight arm and hand muscles; 4) the acceleration in 3D of the hand’s movement.

To specifically inactivate the area of M1 involved in hand movements, we defined M1 hand region

where single pulses of TMS stimulation elicited motor evoked potentials with the largest amplitude and

the highest probability, and we applied series of burst (3 pulses with 33.3 ms interval during 33.3

seconds), corresponding to theta burst stimulation.

Preliminary results show a decrease of EMG activity of hand and arm muscles, as well as a decrease

of grip and load forces applied to perform the task at higher level of resistance, associated with longer

forces application durations, and also changes of acceleration during the displacements of the hand to

perform the task.

In future studies, inactivating other motor areas involved in the control of manual dexterity, such as the

premotor cortex and the supplementary motor area, should assess the exact implication of those

areas in manual dexterity.

References [1] Ying-Zu Huang, Y.-Z., Edwards, M.J., Rounis, E., Bhatia, K.P. and Rothwell, J.C. (2005). Theta Burst

Stimulation of the Human Motor Cortex. Neuron, 45, 201–206.

[2] Cardenas-Morales, L., Nowak, D.A., Kammer, T., Wolf, R.C. and Schonfeldt-Lecuona, C. (2010). Mechanisms and Applications of Theta-burst rTMS on the Human Motor Cortex. Brain Topogr, 22, 294-306.

Short Biography After her formation in Psychology and Philosophy, Mélanie Kaeser made her PhD Thesis in Prof. Eric

M. Rouiller’s laboratory of Neurophysiology of Action and Hearing in the University of Fribourg in

collaboration with the Neurosurgical Research Group of Drs. Jocelyne Bloch and Jean-François

Brunet in the CHUV hospital in Lausanne. Her main topic was the assessment of autologous brain cell

transplantation therapy to enhance functional recovery following motor cortex lesion affecting manual

dexterity. She is now Postdoc in Eric Rouiller’s lab, where she continues research on mechanisms

underlying functional recovery following brain injury, as well as therapeutical strategies effects.


A06P

Effects of permanent or reversible inactivation of the hand representation in the primary motor (M1) cortex on precision

grip performance in various motor tasks

J. Savidan1, M. Fregosi

1, E. Fortis

1, C.

Roux

1, A.-D.

Gindrat

1, M. Kaeser

1, E. M.

Rouiller

1,

E. Schmidlin

1

1 Dept Medicine, University of Fribourg

Abstract Fine manual dexterity is a complex motor skill under control of M1 hand area via direct

corticomotoneuronal projections, representing a prerogative of primates. Using transient and

permanent inactivations of M1 hand area in adult macaque monkeys, this study aimed at quantitatively

assessing different aspects of grasping and precision grip movements based on a palette of manual

tasks. A modified version of the “Brinkman board” task assessed the number of pellets retrieved using

precision grip from vertical and horizontal wells, the latter requiring additional wrist rotations. A

modified version of the “Klüver board” task assessed different types of grasping using various

combinations of fingers’ association, by measuring the retrieval time from wells of different sizes. The

“reach and grasp” drawer task assessed the load and grip forces to open a drawer against different

levels of resistance, involving precision grip in a supination movement. The performance during the

modified Klüver board and the drawer task were correlated to EMG activity exhibiting the time course

of involved muscles. A permanent inactivation with ibotenic acid infusion in M1 hand area led to a

dramatic modification of all motor parameters, followed by a slow incomplete functional recovery. In

contrast, transient inactivation produced by repetitive transcranial magnetic stimulation affected more

selectively and more less severely the different motor parameters. The cortical mapping changes

following permanent inactivation was assessed using EEG recordings. These complementary

approaches were developed to elucidate the mechanisms involved in motor recovery and to refine

therapy improving rehabilitation. Because it was proposed that immediate adjacent spared cortical

territories do not underlie the observed functional recovery following M1 hand area permanent

inactivation [1], these complementary approaches will assess the still debated role of the

contralesional M1 hand area, by transient inactivation with muscimol.

References [1] Wyss, A. F., Hamadjida, A., Savidan, J., Liu, Y., Bashir, S., Mir, A., Schwab, M. E., Rouiller, E. M., & Belhaj-

Saif, A. (2013). Long-term motor cortical map changes following unilateral lesion of the hand representation in the motor cortex in macaque monkeys showing functional recovery of hand functions. Restor.Neurol.Neurosci., 31(6): 733-760.

Short Biography Savidan Julie is a PhD student at the Laboratory of Neurophysiology of Action and Hearing directed by

Prof. Rouiller E.M., of Fribourg University. She is graduated from Bordeaux University with a master in

Neuropsychopharmacology and Addictology in 2007. She worked in Genfit, a biotechnological society,

as research technician, to develop an animal model for Parkinson’s disease in 2008. Since 2009, her

PhD topic is to study spinal and cortical injury focused on fine manual dexterity impairment, to improve

understanding of mechanisms underlying injury, the following recovery and the effect of different

molecules as therapy.


A08P

A microcircuit model of primary motor cortex for the precision grip task

F. A. F. Schuler1*, J. Heinzle

2, K. A. C. Martin

1

1 Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland

2 Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich,

Switzerland

Abstract Primary motor cortex (M1) is the main origin of corticospinal and corticonuclear efferents.

Corticomotoneuronal (CM) cells belong to the thick-tufted layer 5 pyramids and have a strategic

position since they are the cortical output cells that target the spinal alpha-motoneurons directly.

These CM projections are particularly important for independent finger movements. In thick-tufted

layer 5 pyramidal cells (L5PC) the back-propagating action potential activated calcium spike firing

provides an important non-linearity in processing long-distance inputs arriving at the apical tuft. Our

modeling work aims at assessing how this characteristic spiking behavior influences the way in which

the different inputs (from premotor cortex, somatosensory cortex, cerebellum and basal ganglia) are

combined in M1.

Beginning with a highly detailed reconstruction of a thick-tufted L5PC in cat striate cortex, which had

been stained in vivo, we collapsed the respective parts of the dendrite to their equivalent cable and

doped it with known conductances. Our model cell reproduces the spiking behavior of the thick-tufted

L5PC from biological in vitro experiments. This reduced model neuron is the core and first stage in the

development of a biologically realistic model of the microcircuit in M1 whose output is the control of the

precision grip. The reference biological data are the anatomical connectivity data and the extracellular

recordings in M1 made by Muir and Lemon [2] for the precision grip task. Our goal is to contribute to a

mechanistic understanding of what M1 does that will contribute in the long term to applications like

brain-machine interfaces for human patients.

Acknowledgements: FS is supported by a SNSF grant, anatomical reconstructions by SNF Sinergia

and HFSP.

References [1] Larkum, M.E., Zhu, J.J. and Sakmann B. (1999). A new cellular mechanism for coupling inputs arriving at

different cortical layers. Nature, 398(6725): 338-41.

[2] Muir, R.B. and Lemon, R.N. (1983). Corticospinal neurons with a special role in precision grip. Brain Res., 261(2): 312-6.

Short Biography FS got a medical degree from the University of Zurich in 2009. Subsequently, he worked in the

Ospedale Civico in Lugano as an intern for 2 years including the stroke unit and the neurological ward.

The neurological clinic of the Ospedale Civico is the reference center for acute neurological diseases

of the canton of this conference location. Since 2012 he is a PhD student at the Institute of

Neuroinformatics in Zurich, exploring in more detail the neural basis of movements.


A09P

Neural correlates of passive forefinger kinematics: effects of amplitude, velocity and direction

J. Dueñas1*, J. Sulzer

1, P. Stämfli

2,5, M.-C. Hepp-Reymond

3, S. Kollias

4, E. Seifritz

5,

R. Gassert1

1 Rehabilitation Engineering Laboratory, ETH Zürich

2 MR-Center of the Zürich University Hospital for Psychiatry and the Department of Child and Adolescent

Psychiatry, University of Zürich 3 Institute of Neuroinformatics, University of Zürich and ETH Zürich

4 Department of Neuroradiology, University Hospital Zürich

5 Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich

Abstract While the differential neural responses to passive and active movement have been well studied [1,2],

there is little understanding about the relationship between the degree of passive movement and brain

activity. The sense of position and movement involves the integration of different sensory modalities,

some of which are mostly sensitive to dynamic movement, while others encode relative position. Thus,

we hypothesized that these parameters are represented differently in the brain during passive

forefinger movement. To confirm this assumption, we measured blood-oxygen-level dependent

(BOLD) signal using functional magnetic resonance imaging (fMRI) in response to parametric changes

in passively induced forefinger kinematics in a 2x3x3 factorial design. Nineteen right-handed healthy

participants were exposed to combinations of forefinger flexion and extension imposed by an MR-

compatible robotic manipulandum [3], which also measured forefinger interaction forces. Each subject

was exposed to three levels of amplitude (10, 20 and 40% of maximum aperture) and three velocities

(20, 40 and 80% of maximum aperture/sec), separately in flexion and extension. We found that

contralateral primary and bilateral secondary somatosensory regions, as well as contralateral insula

and ipsilateral cerebellum were positively linearly correlated with increases in passive movement

velocity. This relationship also showed higher sensitivity during extension movement. Changes in

amplitude did not show a linear relationship with BOLD response. This can be associated with the

neural representation of muscle spindles, as they are mainly responsible for dynamic movement

perception [4]. These insights will provide a greater understanding of the neural representation of

kinematic variables, which could lead to improved therapeutic strategies for severely impaired patients

following brain injury.

References [1] T. Mima , et al. Brain structures related to active and passive finger movement in man. Brain vol. 122 no. 10

pp 1989-1997, 1999

[2] S. Francis, X. et al. fMRI analysis of active passive and electrically stimulated ankle dorsiflexion. NeuroImage, vol 44, no. 2, pp. 469-479, 2009

[3] R. Gassert, et al. MRI/fMRI-compatible robotic system with force feedback for interaction with human motion, IEEE/ASME Transactions on Mechatronics, vol 11 no. 2 pp 21, 224, 2006

[4] P. Cordo, et al. Contributions of skin and muscle afferent input to movement sense in the human hand. J Neurophysiology. 105: 1877-88. 2011.

Short Biography Julio Duenas obtained his B.Sc. in biomedical engineering from the Universidad Iberoamericana,

Mexico in 2009. He then worked as research engineer at EPFL under the supervision of professor

Olaf Blanke. He obtained his M.Sc. in Robotics, Systems and Control from ETH Zurich in 2013. He is

currently a research assistant at the Rehabilitation Engineering Lab at ETH Zurich working in the field

of neuroscience robotics.


A11P

Area 3b neuronal responsiveness and hand use recovery after spinal cord injury in monkeys

H.-X. Qi1*, J. L. Reed

1, O. A. Gharbawie

1, J. H. Kaas

1

1 Vanderbilt University, Nashville TN, USA

Abstract It is well established that following damage to sensory inputs, the spared inputs expand their territory

to the deprived zones in the target cortex. Studies are lacking, however, to quantify response

properties of reactivated cortical neurons in the somatosensory system in monkeys and evaluate their

relationships to functional recovery. We hypothesized that cortical reactivation was responsible, at

least in part, for behavioral recovery after sensory loss. To test this hypothesis, we first train monkeys

to perform reach-to-grasp tasks. When success scores reached a plateau, a unilateral dorsal column

(DC) section at the C4-6 level of the spinal cord was made on the same side as the preferred hand in

5 monkeys. Five to 13 weeks after behavioral recovery, neural activities across the hand

representation of area 3b were recorded with a 100-electrode array. This recording system offers the

capacity to examine simultaneously the spikes from populations of neurons distributed across cortex

with a known spatial arrangement, allowing us to visualize the cortical population or ensemble activity

from an array of electrodes [1]. We found that even after extensive lesions, performance on reach-to-

grasp tasks returned to pre-lesion levels; and the spared inputs from the hand expanded their territory

to the deprived zones in area 3b. The neuronal response magnitudes to tactile stimulation on the hand

in the partially deprived cortical region were usually weak, as reflected by slightly lower firing rates and

slightly longer response latencies. Some digit representations were abnormal, such that receptive

fields of presumably reactivated neurons were larger and more often involved discontinuous parts of

the hand compared to controls (n = 5). We conlcude that the reactivation of neurons with near-normal

response properties and the recovery of near-normal somatotopy likely supported the recovery of

hand use [2].

References [1] Reed, J.L., Pouget, P., Qi, H.-X., Zhou, Z., Bernard, M.R, Burish, M.J., Bonds, A.B. Kaas, J.H. (2008).

Widespread spatial integration in primary somatosensory cortex. Proceeding National Academy Sciences U. S. A. 105(29): 10233-10237.

[2] Qi H-X., Reed J.L., Gharbawie O.A., Burish M.J., Kaas J.H. (2014). Cortical neuron response properties are related to lesion extent and behavioral recovery after sensory loss from spinal cord injury in monkeys. Journal of Neuroscience, 34(12):4345-4363.

Short Biography I, Hui-Xin Qi, obtained M.S. degree (1988) with Dr. Bin Wang from Beijing Normal University, China,

and Ph.D. degree (1996) with Professor Dr. Marie-Claude Hepp-Reymond from Zurich University,

Switzerland. My postdoctoral training was with Dr. Jon H. Kaas in Vanderbilt University, USA.

Currently, I am a Research Assistant Professor in Vanderbilt University. For the past 2 decades, my

main research interests have been focused on the anatomical and functional organization of the motor

and somatosensory systems in normal, amputated, and spinal cord injured primates. Our ultimate goal

is to uncover the mechanisms behind the plasticity after injury.


A12P

Mirror properties of macaque intra-cortical LFP

S. Waldert1, R. Philipp

1, G. Vigneswaran

1, R. N. Lemon

1, A. Kraskov

1*

1 UCL Institute of Neurology, Queen Square, London

Abstract It is well established that neuronal activity of cortical motor network of motionless observers is

modulated by actions performed in front of them. Individual neurons with such properties are called

mirror neurons and were discovered in macaque ventral premotor cortex (PMv). They have recently

also been found in macaque primary motor cortex (M1) [1]. Since human EEG/MEG studies also

described changes in beta oscillations in cortical motor areas during observation of an action [2], we

investigated the mirror properties of the intra-cortical LFP recorded in hand regions of macaque PMv

and M1 during action observation, i.e. while an experimenter grasped one of three different objects.

Monkey arm/hand EMGs and eye movements were recorded simultaneously with LFPs.

LFP in PMv and M1 was clearly modulated by action observation. Modulations during observation

were found in the low-pass filtered LFP (<5Hz) mainly when the experimenter grasped an object but

also during release. Beta power decreased during observation of the grasping movement and

increased or remained low during observation of the hold period. Modulations of LFP during

observation were weaker than during execution but correlated in time. They differed between M1 and

PMv and might indicate non-overlapping functional networks for execution and observation. Decoding

LFPs during execution revealed more information about grasp-type in M1 than PMv-LFPs; decoding

was reduced during observation.

Modulations in the LFP <5Hz during observation corroborate the genuine neuronal origin of this signal

component, which has been shown to be a suitable control signal for BMIs.

Supported by Wellcome Trust, Marie Curie Postdoctoral Fellowship (S.W.)

References [1] Vigneswaran,G. Philipp,R. Lemon, R.N. and Kraskov, A (2013). M1 Corticospinal Mirror Neurons and Their

Role in Movement Suppression during Action Observation. Current Biology, 23: 236-243.

[2] Hari, R., Forss, N., Avikainen, S., Kirveskari, E., Salenius, S., and Rizzolatti, G. (1998). Activation of human primary motor cortex during action observation: a neuromagnetic study. Proc. Natl. Acad. Sci. USA 95, 15061–15065.

Short Biography Alexander Kraskov is Wellcome Trust Senior Research Fellow at Sobell Department of Movement

Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London. He was

originally trained in physics in Russia and received his PhD in statistical physics and data analysis

methods in Germany. He recently completed his last postdoctoral training in non-human primate

electrophysiology with Prof Roger Lemon and started his own lab supported by Wellcome Trust.


A14P

Variability statistics of motor cortical spiking activity revealed during resting state and motor behavior

A. Riehle1,2,3

*, T. Brochier1, S. Grün

2,3,4

1 Institut de Neurosciences de la Timone (INT), CNRS - AMU, Marseille, France

2 RIKEN Brain Science Institute, Wako-Shi, Japan

3 Inst of Neuroscience & Medicine (INM-6), Research Center Jülich, Jülich, Germany

4 Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, GermanyAbstract

Abstract Understanding the nature and origin of neuronal variability is essential for our understanding of

information processing in cortical networks. To analyze variability in spiking activity we used 3

measures: (i) The coefficient of variation (CV) of inter-spike intervals (ISIs) measures the (ir)regularity

of a sequence of spikes. Because it largely overestimates irregularity for firing rate changes, we use a

local measure, the CV2 [1]. (ii) The Fano factor (FF), computed as the variance of spike counts

divided by their mean, expresses the spike count variability across trials of a same experimental

condition. (iii) We calculate the serial rank-order correlation (SRC) between neighboring ISIs as a

measure of deviation from a renewal process [2]. We recorded simultaneously the spiking activity of

80 to 160 neurons using Utah arrays chronically implanted in motor cortex of 2 monkeys. We analyzed

data recorded during a wakefulness resting-state condition (non-behavior) or a delayed reach-to-grasp

task (behavior [3]). We found across all neurons a strong negative correlation between mean firing

rate and mean CV2. However, if correlating for each neuron rate and CV2 in sliding windows,

correlation is not significant in ~56% of the neurons, negative in ~34% and positive in ~10% of them.

Furthermore, neurons with a significant SRC show a strong negative correlation between SRC and

CV2. We found that SRC is mainly positive and significant in almost all neurons during non-behavior,

whereas in only 20-30% of them during behavior. During behavior, FF is negatively correlated with

firing rate and positively with CV2, and is lower in neurons with a negative than a positive SRC. When

separating behavior in periods of wait and periods of movement, CV2 and firing rate are significantly

lower and FF is significantly higher during wait than movement. We will discuss how these variability

measures are related to behavior and the functional organization of motor cortical networks.

Funding: Agreements Riken-CNRS and FZJülich-CNRS, ANR-GRASP, EU FP7-ICT-2009-6 -

BrainScaleS, Helmholtz Portfolio SMHB, CNRS (PEPS, Neuro_IC2010)

References [1] Holt et al. (1996) Neurophysiol 75: 1806-1814.

[2] Perkel et al. (1967) Biophys J 7: 391-418.

[3] Riehle, Wirtssohn, Grün, Brochier (2013) Frontiers in Neural Circuits 7: 48

Short Biography I'm a Research Director at the CNRS in Marseille. By combining approaches from cognitive and

theoretical neuroscience and recording massively parallel neuronal activity in monkey motor cortex, I

study higher cognitive motor processes and investigate the temporal dynamics of cooperative,

distributed cortical networks.


A16P

From vision to action: a comparative population study of hand grasping areas AIP, F5, and M1

S. Schaffelhofer1*, H. Scherberger

1,2

1 Deutsches Primatenzentrum GmbH, 37077 Göttingen, Germany

2 Department of Biology, University of Göttingen, D-37077 Göttingen, Germany

Abstract Hand grasping requires the transformation of visual object information into corresponding hand

actions. In the primate brain these processes are linked to area AIP (anterior intraparietal cortex), F5

(ventral premotor cortex) and M1 (primary motor cortex). Although these areas demonstrate selective

responses when hand movements are planned or executed, it is up to now unclear how visual and

motor information is encoded on the neuronal population level.

To address this question, we trained two macaques to grasp up to 50 different objects in a delayed

reach-to-hold task. In this, we measured the kinematics of hand and arm together with spiking activity

recorded from up to 300 single- and multi-units using microelectrode arrays. The high variation of

visual stimuli and motor responses in this task allowed us separating visual attributes of objects from

motor features of the hand. Canonical variant and hierarchical cluster analysis demonstrated a

dominant visual role of AIP during both the planning and execution epoch. The neural population

separated the objects primarily based on their shape and secondarily on their size. Furthermore, we

found indicators for the processing of object affordances relevant for grasping. In contrast to AIP, we

could identify in F5 a distinct motor representation that encoded the objects in motor terms. However,

the highest correspondence to the recorded hand kinematics was observed in the M1 population

activity that closely matched the multi-joint space of the hand and arm.

Together our results demonstrate distinct roles of AIP, F5, and M1 at the population level that are

highly relevant for understanding how visumotor transformations are processed in the brain.

References [1] Rizzolatti G, Luppino G (2001). The cortical motor system. Neuron 31, 889-901.

[2] Schaffelhofer S, Scherberger H (2012) A new method of accurate hand- and arm-tracking for small primates. Journal of neural engineering 9, 026025.

Short Biography I obtained my Diploma degree at the University of Applied Sciences Linz, Austria, where I decoded the

spatial position of rats based on place cell activity. After my Master thesis I worked as in the research

and software development department of Guger technoliges OEG, where I have developed neural

interfaces. Since 2009, I am PhD student at the German Primate Center in Göttingen, where I have

investigated visuo-motor transformations in macaque grasping areas AIP, F5 and M1.



NEUROSCIENCE

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B02P

Third arm for surgeon: Embodiment study in virtual reality

E. Abdi1*, M. Bouri

1, E. Burdet

2, H. Bleuler

1

1 EPFL Lausanne

2 Imperial College London

Abstract Surgeons need assistance in almost every type of operation from open to laparoscopic and

teleoperated surgery. The assistant should either work in parallel with the surgeon (e.g. holding the

endoscope in laparoscopic surgery) or help him in performing a task that needs more than two hands

(e.g. suturing, keeping tissue out of the way). The need of team work in these circumstances can be

the source of error and efficiency decrease especially if the assistants are novice and/or unfamiliar

with the surgeon [2]. In this study we investigate the possibility of having a third arm controlled by the

surgeon to make him/her more independent, autonomous and dexterous [1]. Different experiments,

proposed as games, are done in VR using the Kinect® camera for motion tracking. Three hands

appear on the screen (two of which reproduce the user’s two real hands and the third one controlled

by the foot). The task is to manipulate three objects simultaneously. The learning time (time needed to

totally succeed in realization of the task) is measured in each game. The subjects then fill a

questionnaire. The aim is to study the mechanisms of embodiment of three independent hands as

naturally as possible. About 10 subjects have already participated in the experiment. The current

results show a tendency towards the ease of control three hands independently in this way. It is also

reported that practice helps efficiently to enhance sense of ownership towards the third arm in

dynamic experiments. Currently testing of this setup goes on with more people and with the

development of the VR environment approaching realistic surgical situations. This opens up the

possibility of new surgical techniques, frees space in the operating room and lowers risks and costs,

as less assistive personnel and less communication will be needed during surgery.

References [1] Llorens-Bonilla, B., Parietti, F., & Asada, H. H. (7-12 Oct. 2012). Demonstration-based control of

supernumerary robotic limbs. Paper presented at the Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on.

[2] Nurok, M., Sundt, T. M., & Frankel, A. (2011). Teamwork and Communication in the Operating Room: Relationship to Discrete Outcomes and Research Challenges. Anesthesiology Clinics, 29(1), 1-11.

Short Biography Elahe Abdi is a second year PhD assistant at EPFL. Her research lies in the joint domain of surgical

robotics and neuroscience. Currently she works on the concept of having a third arm for the surgeon.

She has completed her bachelor in Mechanical Engineering and master in BioMechanics at University

of Tehran and Sharif University in Iran.


B05P

Perception of co-speech hand gestures in aphasic patients: Visual exploration during the observation of dyadic dialogues

B. C. Preisig1*, N. Eggenberger

1, G. Zito

2, R. M. Müri

1,3

1 Perception and Eye Movement Laboratory, Departments of Neurology and Clinical Research, Inselspital,

University Hospital Bern, Switzerland 2 ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland

3 Division of Cognitive and Restorative Neurology, Department of Neurology, Inselspital, Bern University Hospital,

and University of Bern, Switzerland

Abstract Co-speech gestures can be referred to as hand movements that accompany spontaneous speech

during conversation. Aphasia is an acquired language disorder which restricts verbal communication.

Previous research in aphasic patients mainly focused on gesture production and neglected gesture

perception. The few studies, which examined gesture perception in healthy subjects, used monologue

stimuli (e. g. Gullberg & Holmqvist, 2006). The present study aimed to investigate the perceptive

process of co-speech gestures during the observation of dyadic dialogues. It was expected that

aphasia and the presence of co-speech gestures modulate visual exploration behavior.

Twenty-three aphasic patients and 23 age- and sex-matched healthy control subjects participated in

the study. Visual exploration behavior was assessed by means of a contact-free infrared eye-tracker

while subjects were watching videos depicting spontaneous dialogues. The factors co-speech gesture

(present and absent), gaze direction (to the speaker and to the listener), and region of interest (ROI),

including hands, face, and body, were analyzed separately for the depending variables cumulative

fixation duration and mean fixation duration.

We found a co-speech gesture x gaze direction x ROI interaction, indicating that the presence of a co-

speech gesture encouraged subjects to look at the speaker’s hands. Further, there was a significant

main effect of group and a significant. gaze direction x ROI x group interaction revealing that controls

showed longer cumulative fixation duration on the speaker’s face than aphasic patients.

Co-speech gestures seem to guide the observer’s attention towards the speaker, the source of

semantic input. This is a finding that could be of particular importance for aphasic patients who

showed reduced cumulative fixation duration on the speaker.

References [1] Gullberg, M., and Holmqvist, K. (2006). What speakers do and what addressees look at: Visual attention to

gestures in human interaction live and on video. Pragmatics and Cognition, 14(1), 52-82.

Short Biography Basil Preisig obtained a Master’s degree in Psychology with in-depth studies in neuropsychology and

clinical psychology at the University of Bern in 2012. Subsequently, he started an interdisciplinary PhD

in Neuroscience at the Medical Faculty. His research interests are nonverbal communication

strategies such as gesture production and gesture perception in healthy and brain damaged

populations.


B08P

Enumeration in children with developmental coordination disorder

A. Gomez*1,2

, M. Piazza2, A. Jobert

2, G. Dehaene

2, C. Huron

2

1 Center for Cognitive Neuroscience (CNC), UMR 5229, Université Claude Bernard Lyon 1 (UCBL), Ecole

Supérieure du Professorat et de l’Enseignement (ESPE). 2 Cognitive Neuroimaging Unit, INSERM, U992, CEA/SAC/DSV/DRM/NeuroSpin.

Abstract Children with Developmental Coordination Disorder (DCD) are impaired in gross and fine motor

functions and balance. DCD interferes with academic achievement and daily life. It is associated with

persistent academic difficulties, in particular within mathematical learning. In the present study, we

aimed to understand their impairments in numerical cognition using an approach that taps very basic

numerical processes: 1) Subitizing, related to the object-tracking system, OTS, which allows to

accurately and effortlessly perceive small numerosity; 2) estimation, related to the Approximate

Number system, ANS, which allows to estimate objects and obeys Weber’s law; and 3) counting

abilities, an early cultural acquisition, which uses simple procedures and principles (e.g., cardinality).

We employed two verbal tasks to assess visual enumeration in forty 7-10 years-old children with or

without DCD. On both tasks, children enumerated the visual sets with numerosity within or beyond the

classical subitizing range with a flashed or an untimed presentation. The flashed enumeration task

showed the existence of a reduction of the OTS in children with DCD. Discrimination of larger

numerosity showed a typical ratio effect but a greater imprecision in DCD –as revealed by the Weber

fraction-, hence suggesting a greater imprecision of the ANS. The unlimited time enumeration task

showed that DCD children like controls use a serial counting routine and understand basic principles

necessary to perform counting. However, their counting procedure is very inefficient. Errors analysis

showed that DCD children skipped or double counted objects. As gestures contribute to the

development of counting skills, dysfunctional coordination seems to prevent children with DCD from

keeping track of counted objects. Consequently, DCD children may fail to benefit from counting

experience to refine their ANS. Those abilities and impairments in basic numerical processes in DCD

children should be taken into account to develop future remediation tools.

References [1] Sella, F., Lanfranchi, S., and Zorzi, M. (2013). Enumeration skills in Down Syndrome. Research in

developmental disabilities, 34(1): 3798-3806.

Short Biography Alice Gomez is a lecturer in Cognitive Psychology at the University Claude Bernard Lyon 1 and a

member of Angela Sirigu’s team in the Center for Cognitive Neuroscience (CNC). She completed a

PhD in cognitive psychology in Grenoble (LPNC) on the interplay between spatial processing and

episodic memory using behavioral and fMRI approach with a particular interest on developmental and

acquired amnesia. Her interest in the neurocognitive mechanisms of developmental disorders led her

to perform a postdoc in Stanislas Dehaene’s lab. She studied cognitive and cerebral dysfunctions of

children with Developmental Coordination Disorder with a particular interest on mathematics.


B09P

Standardization of american sign language trials for comparison of EMG and joint angles

M. de Bruin1*, S. J. Wohlman

2, W. M. Murray

1,2

1 Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago IL, USA

2 Department of Biomedical Engineering, Northwestern University, Evanston IL, USA

Abstract Simultaneous recordings of EMG and kinematics during complex hand movements are necessary for

a broader understanding of how the intact hand is naturally controlled. Whereas in gait analysis,

movement segments are often lined up using foot switches, in “open-loop” movements this method

cannot be used. Objective determination of start- and endpoints of a movement has been proposed

based on on multiple sources of information [1], for example hand posture, movement velocity, and

time. In this study, we aimed to develop an

objective method to align American Sign

Language (ASL) movements in time, enabling

comparison of these trials within and between

subjects.

Intramuscular EMG data were recorded in seven

subjects using bipolar wire electrodes. EMG

signals were collected at 2000 Hz, filtered, and

normalized to maximum voluntary contractions.

Joint angles were measured using a Cyberglove.

Subjects were instructed to closely follow a video

of a hand gesturing an “L” or a “D” in ASL,

starting and ending each posture with completely

stretched fingers (Fig 1).

Specific start and endpoints of the captured hand

movement were identified by solving for the

maximum values of mathematical functions that

weighted joint position and joint velocity of the

MCP joint of the middle finger, and the time

weighted either early (Eq. 1a) or late (Eq. 1b) in

the trial.

(1.a)

3

max

3

3

max

3

111

1MCP

MCP

i

MCP

MCP

i

n

i

MAXStart

(1.b)

3

max

3

3

max

3

111

MCP

MCP

i

MCP

MCP

i

n

i

MAXEnd

We have successfully implemented this method to define a standardized motion cycle for analyzing

complex hand movements using the repeatable, standardized neutral start and end position (Fig 1).

We expect this method to enhance quality of data analysis and interpretation of data across different

gestures and tasks as well as across different subjects.

References [1] Schot, W.D., Brenner, E., Smeets, J.B. (2010). Robust movement segmentation by combining multiple sources

of information. Journal of Neuroscience Methods, 187:147-155.

Short Biography Marije finished her MSc in Human Movement Sciences at VU University, Amsterdam, and obtained

her PhD at the Academic Medical Center in Amsterdam focusing on musculoskeletal adaptation in the

spastic arm of cerebral palsy patients. She is currently appointed as a Post-doctoral Research

Associate at the Sensory Motor Performance Program of RIC, focusing on hand biomechanics.


B10P

Cues in the execution of everyday sequential tasks: Support or interference?

B. Drozdowska1*, A. Arnold

1, E. Fringi

1, A. Worthington

1, A. Wing

1, P. Rotshtein

1

1 University of Birmingham, UK

Abstract The aim of the present study is to determine what role cues can play in the execution of everyday

sequential actions. The influence of two main factors is taken into account – the impact of deficits

presented after brain injury, the type of sequence that is being cued, and the level of cueing. Twenty-

seven control participants and ten patients were assessed on their performance of tea-making in

conditions involving either instructions given at goal level (e.g. make a cup of tea) or at the sub-goal

level (e.g. add water to kettle). Two action sequences were tested in each participant (their familiar

habitual tea & action sequence, and a non-familiar sequence). The results suggest that controls made

more errors in the non-routine task; with cues at sub-goals enhancing performance. In contrast, in the

task involving routine behaviours controls were less able to suppress an automatic sequence

execution when cues were given at the sub-goal level, making more errors in this condition. Patients

responded differently to cues depending on the exhibited type of impairment. Patients with sustained

attention deficits struggled to adhere to cues when given at sub-goal level, in contrast patients with

action disorganization deficits made most errors when cues were at the goal level. Results are

discussed in view of developing intervention strategies for patients, as well as conclusions regarding

mechanisms underlying sequential behaviours themselves.

References [1] Cooper, R. P., & Shallice, P. (2000). Contention scheduling and the control of routine activities. Cognitive

Neuropsychology, 17(4), 297 – 338.

[2] Schwartz, M. F. (2006). The cognitive neuropsychology of everyday action and planning. Cognitive Neuropsychology, 23(1), 202-221.

Short Biography Graduated in 2011 from the Warsaw School of Social Sciences and Humanities in Poland with a

Master's Degree in Psychology. Graduated in 2014 from the University of Birmingham with a Master's

Degree in Brain Injury Rehabilitation. Currently employed at the University of Birmingham as a

research associate on the CogWatch project, aiming at cognitive rehabilitation of apraxia and action

disorganization syndrome.


B11P

The cognitive and neural correlates of apraxia

R. Evans1*, W.-L. Bickerton

1, G. Humphreys

1, J. K. Lau

1, A. Worthington

1, P. Rotshtein

1

1 University of Birmingham, UK

Abstract The aim of this study is to understand the neural and cognitive systems underlying apraxia using

function-lesion mapping of gesture tasks. Data from 293 sub-acute stroke survivors included: a

comprehensive neuropsychological battery (Birmingham Cognitive Screen, BCoS) to test the cognitive

domains of praxis, memory, attention, language and number; the Barthel Index and brain images to

assess the integrity of grey matter. Apraxia was assessed using three gesture tasks imitation,

production and recognition. Our first analysis aimed to explore relations between the praxis gesture

tasks and the other cognitive-behavioural measures. We found significant relationships between

apraxia and language, calculation and spatial attention (neglect). Barthel as a measure of functional

independence correlated with language, attention, memory and mood, however the largest

correlations were with the praxis tasks especially gesture production and imitation. The three gesture

praxis also highly correlated. Therefore to be able to tease apart the underlying processes we

conducted principal component analysis. This revealed three components: a shared component

across all three gesture tasks; a semantic component; and visual-verbal component. A follow up

voxel-based morphometry analysis showed that the shared component was linked with lesions to

bilateral middle temporal lobes and the left inferior parietal lobe. Impairment in semantic component

correlated with lesions to the posterior cingulate gyrus and right inferior parietal lobe. In contrast,

impaired non-semantic processing (imitation) was associated with lesions in the left anterior cingulate

gyrus, left hippocampus and left inferior parietal lobe. Poor performances on tasks relying on

processing of verbal inputs were related to lesions in the middle temporal gyrus, anterior cingulate

gyrus and thalamus; whereas deficits with processing of visual inputs were correlated with lesions in

the cerebellum, right occipital pole, right precuneus and bilateral cuneus regions. Overall the data

support existing cognitive models for apraxia, demonstrating that apraxia is a hetrogneous syndrome

that can arise from lesion to multiple dissociated networks. However there is also a strong shared

component that was associated with lesions to left inferior parietal and bilateral temporal lobes.

References [1] Bickerton, W-L., Riddoch, M. J., Samson, D., Bahrami Balani, A., Mistry, B. & Humphreys, G. W. (2012).

Systematic assessment of apraxia and functional predictions from the Birmingham Cognitive Screen (BCoS). Journal of Neurology, Neurosurgery and Psychiatry, 83, 513-521.

Short Biography Rachel Evans has a background in investment and finance and moved into psychological research in

2003. She is currently employed by the University of Birmingham and facilitates Stroke research within

the NHS.


B13P

Illusory ownership and agency induced by passive and active pinching

M. Akselrod1,2

*, R. Martuzzi2, J. Sulzer

3,1, O. Blanke

2, R. Gassert

1

1 Rehabilitation Engineering Laboratory, Federal Institute of Technology of Zurich, Zurich, Switzerland

2 Laboratory of Cognitive Neuroscience, Federal Institute of Technology of Lausanne, Lausanne, Switzerland

3 REWIRE Lab, University of Texas, Austin, USA

Abstract Bodily self-consciousness involves the feeling that our body belongs to us, termed “body ownership”,

as well as the feeling that we control and generate the actions of our body, termed “agency” [1] and is

believed to originate from the integration of congruent multisensory signals. By means of conflicting

multisensory information, bodily self-consciousness has been experimentally manipulated to induce a

feeling of ownership for an external object such as a rubber hand [2] or a virtual avatar [3][4]. Most

studies investigating illusory ownership for external objects employed visuo-tactile stimuli, not

involving any overt motion by the participant.

In this study, we developed a novel paradigm combining robotics and virtual reality to investigate

illusory effects induced by visuo-proprioceptive and visuo-motor conflicts. Subjects either actively

moved a robotic finger interface with their right index finger in a pinching movement against the

opposing thumb, or were passively moved by the robotic device. The displayed virtual avatar

performed a pinching action either synchronous or asynchronous with the real hand, and with either

the same hand (right) or the other hand (left) (2x2 design). Following each active/passive exploration

period, subjects were asked to rate their feeling of ownership over the virtual hand and of agency for

the pinching movement. In data collected from 27 subjects, we found that the feeling of ownership for

the virtual hand is maximized when the movements of the real and virtual hand are synchronized

(ppassive<0.0001, pactive=0.001) and performed with the same hand (ppassive=0.0006, pactive<0.0001).

Furthermore, a group of participants reported a new type of illusory effect, not reported before in the

literature: the feeling of illusory agency during passive pinching (psynchrony<0.0001, pcongruency=0.009).

The results show further evidence that the feelings of ownership and agency are generated and can

be manipulated by congruent sensory-motors signals.

References [1] Schwabe L, Blanke O (2007) Cognitive neuroscience of ownership and agency; Consciousness and Cognition

16:661–666

[2] Botvinick M, Cohen J (1998); Rubber hands 'feel' touch that eyes see; Nature 391:756

[3] Slater M, Perez-Marcos D, Ehrsson HH, Sanchez-Vives MV (2008) Towards a digital body: the virtual arm illusion. Front Hum Neurosci 2:6

[4] Lenggenhager B, Tadi T, Metzinger T, Blanke O (2007); Video ergo sum: manipulating bodily self-consciousness; Science 317(5841):1096-9

Short Biography Michel Akselrod obtained a BSc (2010) and a MSc (2012) in Life Sciences from the Ecole

Polytechnique Fédérale de Lausanne (EPFL). During his masters, he specialized in neurosciences

and conducted his master thesis at the University of Houston, Texas USA, under the supervision of

professor Haluk Ogmen (UofH, USA) and professor Michael Herzog (EPFL, CH), studying the visual

system using fMRI. In September 2012, he started his PhD in a joint program between the laboratory

of Cognitive Neuroscience at EPFL, directed by professor Olaf Blanke, and the rehabilitation

engineering laboratory at ETHZ, directed by professor Roger Gassert. He is interested in studying how

the representation of the body in the brain can be mapped and modulated with tools such as robotics,

virtual reality and real-time fMRI neurofeedback.


B14P

Neuromodulation averts phantom pain and reinstates the deprived cortex to the sensorimotor system

S. Kikkert1*, M. Mezue

1, J. O’Shea

1, D. Henderson-Slater

2, I. Tracey

1, H. Johansen-Berg

1,

T. R. Makin1

1 FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford

2 Oxford Centre for Enablement, Nuffield Orthopaedic Centre

Abstract Following arm amputation individuals frequently report experiencing vivid sensations of the missing

limb. These ‘phantom’ sensations are often experienced as painful, manifested in an intractable, and

highly debilitating, chronic neuropathic pain syndrome (phantom limb pain, PLP). Neurorehabilitation

approaches, designed to reinstate the representation of the missing hand into the deprived cortex, are

ineffective. We recently showed that the magnitude of chronic PLP positively scales with maintained

representation of the missing hand during voluntary performance of phantom movements, as well as

functional isolation of the deprived cortex. The current study was aimed at modulating PLP using non-

invasive brain stimulation (neuromodulation), in a double blind, counterbalanced and sham-controlled

design.

Twelve unilateral upper-limb amputees suffering from chronic phantom pain underwent twenty minutes

of excitatory (anodal, 1mA) or sham brain stimulation (transcranial direct current stimulation, tDCS) to

deprived sensorimotor cortex while performing a PLP-inducing task. Task-based and resting-state

functional magnetic resonance imaging scans, as well as subjective pain ratings, were obtained prior

to and post neuromodulation. Whereas PLP was increased in the sham condition, excitatory

stimulation of the deprived sensorimotor cortex averted this pain increase. Phantom pain induction

was seen in conjunction with functional isolation of the phantom hand area from the sensorimotor

cortex, and decreased functional connectivity between the intact and phantom hand sensorimotor

cortices. The prevention of PLP increase following excitatory neuromodulation was related to a

functional reintegration of the phantom hand cortex into the sensorimotor system, as reflected in

increased fMRI activation in sensorimotor areas during phantom hand movements.

Our results reveal tDCS as a promising potential tool for the management of phantom limb pain and

highlight the tight coupling between PLP and the sensorimotor system. These results should be taken

into consideration when designing novel therapies to relieve PLP.

Short Biography I am a DPhil student at the neuroimaging center (FMRIB) of the University of Oxford. In my research I

use neuroimaging techniques and non-invasive brain stimulation to study brain plasticity following the

loss of sensory in- and outputs (i.e., amputation). My DPhil is funded by the Medical Sciences

Graduate School Studentship and supervised by Dr Tamar Makin and Prof. Heidi Johansen-Berg. I

am also collaborating with Prof. Christian Beckmann, located at the Donders Institute in the

Netherlands.


NEUROPROSTHETICS

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C01P

Action Reading Machine: A proposal for generalising Fadiga's (1995) method of TMS-induced motor-evoked potentials

S. Vogt1*, S. Linkenauger

1, K. Sakreida

2,3

1 Department of Psychology, Lancaster University, Lancaster, United Kingdom

2 Section Clinical-Cognitive Sciences–Department of Neurology, Medical Faculty, RWTH Aachen University,

Aachen, Germany 3 Department of Neurosurgery, Medical Faculty, RWTH Aachen University, Aachen, Germany

Abstract We propose the further development of Fadiga's MEP method [1], where Transcranial Magnetic

Stimulation (TMS) over M1 is used to induce Motor Evoked Potentials (MEPs) in the related arm

muscles as a 'read-out' of movement-selective motor cortical states. Fadiga et al. [1] first

demonstrated that MEPs recorded during action observation were strongest in those muscles that are

involved in executing the observed action. Whereas initially these action-selective MEPs were

believed to primarily reflect upstream activity in premotor 'mirror' regions, over the last 20 years

evidence has been accumulating for the presence of mirror-like activity in M1. Recently, Vigneswaran

et al. [2] additionally showed that the discharge of 'facilitation-type' neurons was remarkably lower

during observation than during execution, and that 'suppression-type' neurons actually increased firing

during execution. Whilst these findings certainly caution against simply equating cortical motor states

during action observation and execution, we still believe that Fadiga's MEP method can provide a rich

source of information about motor cortical states in human experiments.

First, whereas in previous studies only a small number of muscles was recorded, we suggest

recording from as many hand-, arm- and shoulder muscles as technically feasible (using a 32 channel

EEG system). Second, the most appropriate stimulation intensity, coil shape and size for such a

'shotgun' stimulation approach will need to be identified. Third, suitable stimulation sequences will

need to be piloted for estimating the time course of activation during, e.g., action observation and

execution. And fourth, we propose that pattern classification techniques are used to identify

'fingerprints' of a wide range of individual motor actions. As a result, such an Action Reading Machine

(ARM) could provide researchers with time series of 'activation strength' of multiple action

representations that might be concurrently prepared or simulated (e.g., Vogt et al. [3]).

References [1] Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995) Motor facilitation duringaction observation: a

magnetic stimulation study. J Neurophysiol 73: 2608–2611.

[2] Vigneswaran, G., Philipp, R., Lemon, R.N., & Kraskov, A. (2013). M1 corticospinal mirror neurons and their role in movement suppression during action observation. Curr. Biol. 23, 236-43.

[3] Vogt, S., Di Rienzo, F., Collet, C., Collins, A. & Guillot, A. (2013). Multiple roles of motor imagery during action observation. Front. Hum. Neurosci. 7, article 807.

Short Biography Stefan Vogt is an experimental psychologist and neuroscientist and has widely published on action

observation, imitation learning and automatic imitation. He gained a diploma in Psychology at Münster

University and received his PhD at Bremen University in 1988. He then worked as a Senior

Researcher at the Max-Planck-Institute for Psychological Research in Munich, before joining the

Psychology Department at Lancaster University in 1995. His research is focussed on relationships

between perception, motor imagery, and action, and he uses a range of behavioural and neuroscience

methods, kinematic data, reaction times, and functional magnetic resonance imaging (fMRI).


C02P

Unsupervised segmentation of natural movement for smart prosthetic control

A. Thomik1*, D. Haber

2, A. A. Faisal

1,2,3




Abstract While the mechanical structure of human limbs is well understood, their control, and its computational

implementation are unclear. The window into this problem may lie within the vast amounts of data we

are able to collect from human behaviour. Yet, analysing very big data sets is a challenge in itself and

state of the art Bayesian approaches can easily become computationally intractable, or reduce to

models so simple that they fail to capture the underlying structure. Here, we present a computationally

simple method for segmenting data into basic actions and exploit this simplified structure to develop a

smarter control algorithm for prosthetic devices based on observing the movement of intact limbs. Our

method combines a series of powerful, yet inexpensive algorithms to achieve segmentation and

clustering of the time series: PCA, approximate Bayesian segmentation and temporal correlation. We

validate our approach on a synthetic data set for which the ground truth is known and show that the

proposed method is extremely robust to noise and parameter variation. Computational complexity

scales linearly with the number of data points. We then apply the method to a large dataset of natural

hand movements recorded from healthy subjects, allowing us to extract subject-independent

movement descriptors. We simulate amputees by removing multiple dimensions of movement

information and show that we can effectively reconstruct this missing data based on the movement

descriptors found with complete data. We achieve significantly better predictions than would be

possible by directly correlating observed and missing data. This work emphasises tight relationship

between our limbs during everyday activities and opens a new pathway for the control of prosthetic

limbs.

References [1] Thomik, A. A., Haber, D., & Faisal, A. A. (2013). Real-time movement prediction for improved control of

neuroprosthetic devices. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on (pp. 625-628). IEEE.






has developed computational methods and experimental paradigms.


C03P

Neural mechanisms of non-invasive brain-machine interfaces control

S. Marchesotti1,2,3

*, R. Martuzzi1,3

, M. L. Blefari1,3

, A. Schurger1,3

, H. Bleuler2, O. Blanke

1,3

1 Laboratory of Cognitive Neuroscience, EPFL, Lausanne, Switzerland

2 Laboratory of Robotic Systems, EPFL, Lausanne, Switzerland

3 Center for Neuroprosthetics EPFL, Lausanne, Switzerland

Abstract Advances in neuroscience and engineering have led to the development of technologies allowing the

control of external devices through real-time decoding of brain activity, using invasive and non-

invasive brain-machine interfaces (BMI).

Non-invasive BMIs based on surface EEG sample from large brain regions but have not the spatial

resolution to describe the mechanisms throughout the brain involved in BMI control.

Here we developed an EEG-BMI-fMRI protocol that allowed us to accomplish real-time, motor

imagery-based, non-invasive BMI based on surface EEG recordings while recording brain activity

using fMRI.

Sixteen healthy subjects participated in the study. BMI was obtained using a 64 channel MR-

compatible EEG, while BOLD signal was acquired using a 3T scanner. A previously used BMI

algorithm based on the real time analysis of the mu-rhythm was adapted for on-line classification.

Participants were instructed to perform lateralized motor imagery with visual feedback: following a

directional cue, they had to control the movement of a cursor by imagining clasping their left or right

hand. Additionally, in half of the trials, the visual feedback was experimentally manipulated by inverting

the direction of the cursor (deviated trials) to investigate the effects related to such visuo-neural

conflicts.

In the left and right motor imagery conditions, fMRI results revealed activations in premotor cortex,

posterior parietal cortex, supplementary motor area, but also in anterior insula and occipital cortex,

with stronger activation in contralateral premotor cortex. Furthermore, contrasting the non-deviated

with the deviated condition revealed activation in the basal ganglia. Our results extend previous EEG-

BMI-fMRI data and present a novel approach that allows to generate BMI control based on EEG

signals recorded during 3T fMRI.

We conclude that when controlling a non-invasive BMI, human subjects rely on a distributed and

bilateral network including the fronto-parietal, visual and insular cortices, and basal ganglia.

References [1] Pfurtscheller, G., Neuper, C., Flotzinger, D., & Pregenzer, M. (1997). EEG-based discrimination between

imagination of right and left hand movement. Electroencephalography and clinical Neurophysiology, 103(6), 642-651.

[2] Guger, C., Ramoser H., and Pfurtscheller, G. (2000) "Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI)." Rehabilitation Engineering, IEEE Transactions on 8.4: 447-456.

Short Biography Silvia Marchesotti obtained a BSc (2008) in Biomedical Engineering and a MSc (2010) in

NeuroEngineering from the University of Genoa. During her master thesis conducted at the Laboratory

of Psychophysics (EPFL), she focused on the technical aspects of the combined use of EEG and

TMS. In 2011, she started her PhD in a joint collaboration between the laboratory of Cognitive

Neuroscience (prof.Olaf Blanke) and the Laboratory of Robotic Systems (prof. Hannes Bleuler) at

EPFL. The main topic of her thesis concerns the investigation of bodily self-consciousness

mechanisms related to Brain Computer Interfaces (BCI) mediated action, as well as to executed

movements.


C04P

Novel Electrocorticographic Brain-Computer Interface Framework for Dexterous Control of a Robotic Hand

D. Sarma1*, J. Wu

1, V. Kumar

2, J. D. Wander

1, J. G. Ojemann

3, R. P. N. Rao

2

1 Department of Bioengineering, University of Washington

2 Department of Computer Science, University of Washington

3 Department of Neurological Surgery, University of Washington

Abstract An implanted chronic brain-computer interface (BCI) could improve the quality-of-life for individuals

with severe neuromuscular deficits by providing a system to enact intuitive, volitional control of

assistive devices. Electrocorticography (ECoG)-based control systems for dexterous upper-body

robotic prostheses have shown promise to fulfill this role. However, current methods for analyzing and

modeling ECoG data are still relatively limited, relying primarily on linearly translating power in single

high-frequency bands (70-200Hz) to the movement of a cursor. To be effective in the long term, a BCI

system for hand prosthetics must be developed such that it can replicate natural human control by

taking advantage of motor primitives, as represented in ECoG data, as well as exploit the brain’s

natural abilities, including its remarkable plasticity, to accomplish fine manipulation tasks. Here we

demonstrate preliminary techniques for dimensionality reduction of ECoG motor and sensory field

potential recordings into synergistic motor primitives and present a framework to control a highly

dexterous and adaptable robotic hand. Utilizing ECoG data recorded at different resolutions (macro,

mini, and micro grid formats) we are able to test a variety of strategies for robotic control as well as

better understand the neural dynamics of innate human hand control. Leveraging recordings from 10

subjects, implanted with clinical (10mm spacing), medium (5mm spacing), and high-resolution (3mm

spacing) platinum subdural ECoG grids (implanted as per separate epileptic clinical considerations),

suggests the presence of synergistic movements of individual digit joints during coordinated grasping

further establishing this type of dimensionality reduction as a robust set of targets for control of the

robotic systems. Through this system, we hope to provide a platform by which to significantly advance

our understanding of the computational basis of human manipulation capabilities and enhance the

utility of ECoG for practical brain-computer interfaces for hand prosthetics.

References [1] Acharya, S., Fifer, M.S., Benz, H.L., Crone, N.E. & Thakor, N.V. (2010).Electrocorticographic amplitude

predicts finger positions during slow grasping motions of the hand. J. Neural Eng. 7, 046002.

[2] Miller, K.J., Zanos, S., Fetz, E.E., den Nijs, M. & Ojemann, J.G. (2009).Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J. Neurosci. 29, 3132–7.

Short Biography Devapratim Sarma is a PhD Candidate in the Department of Bioengineering at the University of

Washington. His current research under the guidance of Dr. Rajesh Rao and Dr. Jeffrey Ojemann

focuses on the computational analysis of electrocorticographic data during dexterous hand

manipulations to better understand the neural circuits and encoding mechanisms related to motor

control. He is also heavily involved with the NSF Center of Sensorimotor Neural Engineering. Prior to

his time in Seattle, Dev received a B.S. in Bioengineering (2008) and a B.S. in Animal Physiology and

Neuroscience (2009) from the University of California, San Diego.


HAPTICS & DEXTERITY

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D01P

Broad-band tactile noise is most effective in improving motor performance and is most pleasant

E. Manjarrez2*, C. Trenado

1, N. Huidobro

2, I. Mendez-Balbuena

2, J. Schulte-Mönting

3,

F. Huethe1, A. Mikulić

1, M.-C. Hepp-Reymond

4, R. Kristeva

1

1 Dept. of Neurology and Neurophysiology, Albert-Ludwigs-University, Freiburg, Germany

2 Institute of Physiology and Dept. of Psychology, Universidad Autonoma de Puebla, Mexico

3 Institute for Medical Biometry and Medical Informatics, University of Freiburg, Germany 4 Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zurich, Switzerland

Abstract Modern attempts to improve human performance focus on stochastic resonance (SR). SR is a

phenomenon in non-linear systems characterized by a response increase of the system induced by a

particular level of input noise, and which is consistent with higher local and long range synchrony.

Recently, we reported that an optimum level of 0–15 Hz mechanical noise applied to the human index

finger improved static isometric force compensation. A possible explanation was a better sensorimotor

integration caused by increase in sensitivity of peripheral receptors. The present study in 10 human

subjects compares SR effects in the performance of the same motor task and on pleasantness, by

applying three mechanical Gaussian-noises on the fingertip receptors (0–15 Hz mostly for Merkel’s

receptors, 250–300 Hz for Pacini’s corpuscles and 0–300 Hz for all). We document that only the 0–

300 Hz noise induced SR effect during the transitory phase of the task. In contrast, the performance

was improved during the stationary phase for all three noise frequency bandwidths. This improvement

was stronger for 0–300 Hz and 250–300 Hz than for 0–15 Hz noise. Further, we found higher degree

of pleasantness for 0–300 Hz and 250–300 Hz noise bandwidths than for 0–15 Hz. Moreover, in

recent experiments in cats we confirmed that spinal neurons activated by Pacini’s corpuscles exhibited

a stronger SR effect for optimal tactile noise than those spinal neurons activated by Merkel’s

receptors. Thus, we show that the most appropriate noise that could be used in haptic gloves is the 0–

300 Hz, as it improved motor performance during both stationary and transitory phases. In addition,

this noise had the highest degree of pleasantness and thus reveals that the glabrous skin can also

forward pleasant sensations.

References [1] Trenado, C., Mikulic, A., Manjarrez, E., Mendez-Balbuena, I., Schulte-Monting, J., Huethe, F., Hepp-Reymond,

M.C., Kristeva, R. (2014) Broad-band Gaussian noise is most effective in improving motor performance and is most pleasant. Frontiers in Human Neuroscience. 8: 22.

Short Biography Elias Manjarrez received the B.Sc. degree in physics, the M.Sc. degree in physiology, and the Ph.D.

degree in neuroscience from the Center for Research and Advanced Studies of the National

Polytechnic Institute CINVESTAV-IPN, Mexico. Since 2001, he has been head of laboratory at the

Institute of Physiology in Puebla, Mexico. Prof. Manjarrez and colleagues published the first

demonstration of these phenomena: internal stochastic resonance (SR) in the central nervous system,

the SR in the motor system and the multisensory SR.


D02P

Upper limb movement deficits in cerebral palsy

E. Jaspers1*, H. Feys

2, K. Desloovere

2, N. Wenderoth

1

1 Neural Control of Movement Lab, ETH Zurich, Switzerland

2 Neuromotor Research Group, KU Leuven, Belgium

Abstract Three-dimensional movement analysis (3DMA) is being increasingly used to evaluate upper limb

movements, though the interpretation of the multiplicity of data remains complex. Here, we introduce a

summary index, the “Arm Profile Score” (APS), to quantify upper limb movement deficits in children

with unilateral cerebral palsy (UCP).

Twenty children with UCP (10.9ys±2.9ys) and 20 individually age-matched typically developing

children (TDC) were assessed using a standardized, reliable protocol for upper limb 3DMA, including

reach, reach-to-grasp and gross motor tasks [1]. Marker tracking was done with the Vicon MX-system

(Oxford Metrics Group, UK) and kinematics were calculated following ISB-recommendations [2] using

customized data analysis tools [3]. In children with UCP, the House-score (functional hand use) and

clinical measures of muscle tone and strength were also assessed.

The APS is calculated as the RMS difference between kinematic data of the individual child with

movement deficits and the average data from TDC and can be decomposed into 13 Arm Variable

Scores (AVS), representing individual joint angles. Significant correlations were found between

House-scores and APS (ρ -0.63 to -0.80), i.e. children with lower House-scores had more deviating

arm kinematics. Increased wrist tone, and decreased forearm and grip strength were also significantly

correlated with higher APS-scores, especially for the reach and reach-to-grasp tasks (ρtone 0.64 to

0.81; ρstrength -0.54 to -0.73). This study provided a sound base to use the APS to evaluate upper limb

movement deficits in children with unilateral CP. Current results suggest that treatment aimed at distal

tone reduction or strength training might influence upper limb kinematic deficits. Further study using

bimanual tasks and the quantitative assessment of mirror movements will deepen our understanding

of the impact of the underlying brain lesion in the motor control of each hand.

References [1] Jaspers et al. Gait Posture 2011; 33: 568-275

[2] Wu et al. J Biomech 2005; 38: 981-992

[3] https://github.com/u0078867/ulema-ul-analyzer

Short Biography I graduated as an MSc in Rehabilitation Sciences and Physiotherapy in 2005 at the KU Leuven

(Belgium), where I continued working and obtained my PhD in Biomedical Sciences in 2011. My

research has resulted in the development of a standardized, reliable protocol for upper limb three-

dimensional movement analysis, including an open-source data analysis tool. In October 2012, I

started at the Neural Control of Movement Lab (ETH Zurich, Switzerland), where I now focus on the

sensorimotor system and how changes in brain networks relate to upper limb deficits in children with

cerebral palsy.

https://github.com/u0078867/ulema-ul-analyzer


D03P

Single finger object manipulation

A.-C. M. Doix1*, B. B. Edin

1

1 Department of Integrative Medical Biology, University of Umeå, Sweden

Abstract When grasping an object between two digits, the load and the grip force are adjusted to the friction

between the skin and the object to anticipate slips and ensure the continuation of the task [1]. Yet,

during object manipulation the digits’ action is alternated and the balance of forces between them is

redistributed. Little is known regarding the sensory inputs or the behavioural consequences of a

fingertip on different contact surfaces when dragging an object (elbow in neutral position). Here, in one

participant, the applied normal (FN) forces by a single digit (digit 2) were investigated when comparing

dragging six times backwards (BK) and forwards (FW) a servoed-controled object having inertial,

viscous and friction properties, with different contact surfaces: sand paper (SP), suede (SU) and silk

(SI). Either the object was dragged with the digit in a constant flexed (FLEX), or extended position

(EXT), or dynamically flexing and extending the digit (DYN). When performing the BK and the FW

drags, a main trial effect (BK and FW: p<0.001), a trial x position (BK and FW: p<0.001) and a trial x

surface interactions (BK and FW: p<0.001) were found. Post-hoc tests revealed that the peak of FN

developed throughout trials significantly differed between all positions in BK (all pairwise: p<0.001)

and FW (all pairwise: p<0.01). Regardless of the surface, when comparing the averaged FN

developed before and after each backwards dragging, it increased of 95% (±40%) after the DYN

(p<0.001), slightly decreased after the EXT of -9% (±15%; p<0.001) but remained steady after the

FLEX position (p=0.215). Our preliminary results suggest a posture-dependant specific control of the

digit, likely attributable to the biomechanics of the metacarpophalangeal joint. More data will be

collected to confirm these results. Also, we will compare the control of the digit 2 when dragging a

servoed-controled object with different mechanical properties.

References [1] Johansson, R.S., Westling, G. (1984). Roles of glabrous skin receptors and sensorimotor memory in automatic

control of precision grip when lifting rougher or more slippery objects, Experimental Brain Research, 56(3): 550–564.

Short Biography Aude-Clémence Doix received a Ph.D. in Health Science from the Norwegian University of Science

and Technology (Trondheim, Norway) and a Ph.D. in Human Movement Science from the University

of Nice - Sophia Antipolis (Nice, France) in 2013. Aude-Clémence Doix currently holds a postdoctoral

position at the Department of Integrative Medical Biology at the University of Umeå where her

research focuses on the neuromuscular mechanisms of the sensorimotor control and muscle

performance in humans.


D05P

Can sensory feedback modulate cross education?

K. L. Ruddy1,3

*, R. G. Carson2,3

, T. J. Carroll5, N. Wenderoth

1,4

1 Neural Control of Movement Lab, ETH, Zurich, Switzerland

2 Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland

3 School of Psychology, Queen's University Belfast, Belfast, UK

4 Motor Control Lab, KU Leuven, Belgium

5 Centre for Sensorimotor Neuroscience, The University of Queensland, Brisbane, Australia

Abstract Cross education is the process whereby training of one limb gives rise to enhancements in the

performance of the opposite, untrained limb. The effect of providing mirrored visual feedback of the

moving limb (during unilateral training), on the cross education of the opposite (untrained) limb, was

investigated in the context of a task that required maximal motor output. In Experiment 1 thirty-six

participants trained under one of three different visual feedback conditions: mirrored visual feedback of

the training (left) limb (n=12); no visual feedback of either limb (n=12); and visual feedback of the

inactive (right) limb (n=12). Training consisted of 300 discrete, ballistic wrist flexion movements

executed as fast as possible. The participant was instructed to maximise the peak acceleration of the

flexion movement. Auditory feedback indicated whether performance improved or did not improve on

each successive effort. Performance of the right limb on the same task was assessed prior to, at the

mid-point and following the period of left limb training. The transfer of performance to the untrained

limb (expressed as a percentage of the improvement of the trained limb) was markedly greater for the

mirrored visual feedback group (136%) than for the no visual feedback group (70%) (W=92, p=0.007,

r=0.45). The group that received visual feedback of the inactive limb did not differ from the group that

received no visual feedback group in terms of the level of transfer. Experiment 2 was a replication of

Experiment 1 with only two groups: Mirror (n=33) and No-Mirror (n=48). In this case, visual feedback

was found to have no effect upon transfer. The outcomes are discussed in terms of theoretical models

that have been proposed to account for the phenomenon of cross education. The possibility for added

benefit from adjunctive vibrotactile feedback is also considered.

Short Biography I graduated from Queen’s University, Belfast in 2010 with a first class honours Bachelor of Science

degree in Psychology, and then progressed to PhD level under the supervision of Professor Richard

Carson. For the final two years of my PhD, I conducted neuroimaging research at Trinity College

Institute of Neuroscience, Dublin, using a combination of resting-state functional magnetic resonance

imaging (rs-fMRI), task-based fMRI, Diffusion Tensor Imaging (DTI) and Transcranial Magnetic

Stimulation to investigate upper limb function. In January 2014 I commenced post-doctoral research at

ETH Zurich in the Neural Control of Movement laboratory under the supervision of Professor Nicole

Wenderoth.


D08P

Comparison of bimanual control manifolds in daily tasks

A. Thomik1*, S. Fenske

1, A. A. Faisal

1,2,3




Abstract Humans continuously interact with their physical environment, mostly by using one or both hands. Yet,

it is not understood how the brain achieves this coordination, and in particular whether the hands

make use of a unified control strategy, which they adapt to the task, or if there are task-dependent

control strategies, which are shared across humans, possibly as result of evolution. Constrained

laboratory settings, however, will only provide reduced insight into these questions and the variety of

natural bimanual movements.

We overcome the limitations of laboratory experiments by recording the hand movements from ten

right-handed subjects performing tasks from daily living (e.g. folding a blanket) using two data gloves.

Using Principal Component Analysis, we compute the task-dependent eigenspace of the left, right,

and combined hands, thus informing us about the different control manifolds subjects use to achieve a

given task, as well as enabling us to quantify the differences between and within the control manifolds

of individual subjects. Interestingly, in tasks requiring only one hand for active manipulation of the

object, while the other acts as a stabiliser, (e.g. opening/closing a bottle), characteristic features of

individual hand movement allow us to identify the subject performing them. However as soon as tasks

become bimanual, the inter subject distinction diminishes. Our findings provide insight into

coordination of hand movements in natural settings and thus contribute to a better understanding of

the development of the human motor system. Furthermore, these results suggest a novel method to

measure the degree of bimanual involvement in specific tasks, and have the potential to inform us

about how movement in the dominant hand influences motion in the other hand.

References [1] Faisal, A., Stout, D., Apel, J., & Bradley, B. (2010). The manipulative complexity of Lower Paleolithic stone

toolmaking. PloS one, 5(11), e13718.






has developed computational and experimental techniques.


D09P

Human grasping in unstructured environments

T. Feix1, I. M. Bullock

1, A. M. Dollar

1*


Abstract We present an analysis of human grasping behavior in

unstructured environments. A head-mounted camera recorded

two housekeepers and two machinists during their regular

work. The full dataset contains 27.7 hours of tagged video and

represents a wide range of manipulative behaviors.

Concerning the frequency of grasps, we found that a relatively

small set of grasps were used the majority of the time. For

80 % of the study duration, only 5 (housekeeper) or 10

(machinist) grasp types were used. Regarding the properties of

the, we found that most of them were well within a graspable

range. That has direct implications for artificial hand design - in

order to grasp 90 % of the objects in our dataset, a hand

should be able to grasps objects 7 cm wide with a mass of

700 g. Finally, we found that 46 % of tasks are constrained,

where the manipulated object is not allowed to move in a full

six degrees of freedom.

Using the object and task properties we were able to predict

the grasp type used with 47 % accuracy. The strongest

predictors of the grasp type are object size, task constraints,

and object mass. We also found that large and heavy objects

are usually grasped by a power grasp, but small and lightweight objects are not necessarily grasped

with a precision grasp.

References [1] Bullock, I.M., Zheng, J.Z., De La Rosa, S., Guertler, C. and I.M., Dollar, A.M (2013). Grasp Frequency and

Usage in Daily Household and Machine Shop Tasks, IEEE Transactions on Haptics, 6(3): 296-308

[2] Feix, T., Bullock, I.M. and Dollar, A.M. (2014). Analysis of Human Grasping Behavior: Correlating Tasks, Objects and Grasps. IEEE Transactions on Haptics, minor revision.

Short Biography Aaron M. Dollar is the John J. Lee Associate Professor of Mechanical Engineering and Materials

Science at Yale. He earned a B.S. in Mechanical Engineering at the University of Massachusetts at

Amherst, S.M. and Ph.D. degrees in Engineering Sciences at Harvard, and conducted two years of

Postdoctoral research at the MIT Media Lab. His research topics include human and robotic grasping

and dexterous manipulation, mechanisms and machine design, and assistive and rehabilitation

devices. He is the recipient of the 2013 DARPA Young Faculty Award, 2011 AFOSR Young

Investigator Award, and the 2010 NSF CAREER Award.


Dexterous sub-classification of human or robotic manipulation

D12P

Classifying dexterous manipulation in human and robotic systems

I. M. Bullock*1, R. R. Ma

1, A. M. Dollar

1


Abstract Understanding and comparing human and robotic dexterous

manipulation requires precise terminology and a classification

scheme designed for that purpose. Our work includes a hand-

centric and motion-centric manipulation classification and

examples of how to apply it in various ways. It is first

discussed how the taxonomy can be used to identify a

manipulation strategy. Then, applications for robot hand

analysis and engineering design are explained. Finally, the

classification is applied to three activities of daily living (ADLs)

to distinguish the patterns of dexterous manipulation involved

in each task. The same analysis method could be used to

predict problem ADLs for various impairments or to produce a

representative benchmark set of ADL tasks. Overall, the

classification scheme proposed creates a descriptive

framework that can be used to effectively describe hand

movements during manipulation in a variety of contexts and

might be combined with existing object-centric or other

taxonomies to provide a complete description of a specific

manipulation task.

References [1] Bullock, I.M., Ma, R.R., Dollar, A.M. (2012). A Hand-Centric

Classification of Human and Robot Dexterous Manipulation. IEEE Transactions on Haptics, 6(2): 129-144

[2] Bullock, I.M., Dollar, A.M. (2011). Classifying Human Manipulation Behavior. IEEE International Conference on Rehabilitation Robotics (ICORR), 532-537.

Short Biography Ian M. Bullock is a PhD candidate studying human grasping and dexterous manipulation at Yale

University. He earned a B.S. in Engineering from Harvey Mudd College (Claremont, CA) and an M.S.

and M.Phil. from Yale University. His research looks at the capabilities of the human hand from the

perspective of trying to improve robotic hand design, haptic interfaces, and rehabilitation efforts.


NEURO-

REHABILITATION

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E01P

Reduction of enhanced physiological tremor via stochastic noise

C. Trenado1*, F. Amtage

1, F. Huethe

1, J. Schulte-Mönting

2, I. Mendez-Balbuena

3,

M.-C. Hepp-Reymond4, E. Manjarrez

5, R. Kristeva

1

1 Dept. Neurology, Univ. Freiburg, Breisacherstrasse 64, 79106 Freiburg, Germany 2 Institute for Medical Biometry and Medical Informatics, Univ. Freiburg, Germany 3 Facultad de Psicologia, Benemérita Universidad Autonoma de Puebla, México

4 Institute of Neuroinformatics, University of Zürich and ETH Zürich, Switzerland 5 Instituto de Fisiologia, Benemérita Universidad Autonoma de Puebla, México

Abstract Enhanced physiological tremor is a disabling condition which could impose a strong limitation to

perform fine movements. Under the light of the hypothesis that “boosting the strength of the peripheral

input pushes the tremor-related spinal and cortical systems closer to anti-phase firing and hence

reduces tremor” [1], the present study aims at investigating whether Gaussian stochastic noise

enables reduction of enhanced physiological tremor accompanied with performance improvement

during a visuomotor task. Specifically, eight subjects with enhanced physiological tremor performed a

visuomotor task requiring to compensate isometrically with the right index finger a static force

generated by a manipulandum on which Gaussian noise (3-35 Hz) was applied. The finger position

was displayed on-line on a monitor as a small white dot which the subjects had to maintain in the

center of a green circle defined as the reference (for methodological details see [2]). EMG from the

active hand muscles and finger position were recorded. The performance was measured by the mean

absolute deviation of the white dot from the zero position. The tremor was identified by the

acceleration in the frequency range 7-12 Hz. Two different conditions were compared: with and

without optimum noise. We found that application of optimum noise reduces tremor (accelerometric

amplitude and EMG activity) and improved the behavioral performance as reflected by the improved

mean absolute deviation from zero. Thus, we provide the first evidence of a significant reduction of

enhanced physiological tremor in the human sensorimotor system due to application of external

stochastic noise.

References [1] Kozelj, S., and Baker, S.N. (2014). Different Phase Delays of Peripheral Input to Primate Motor Cortex and

Spinal Cord Promote Cancellation at Physiological Tremor Frequencies. J. Neurophysiol. Feb 26.

[2] Mendez-Balbuena, I., Manjarrez, E., Schulte-Mönting, J., Huethe, F., Tapia, J.A., Hepp-Reymond, M.C., and Kristeva, R. (2012). Improved Sensorimotor Performance via Stochastic Resonance. J. Neurosci. 32, 12612-12618.

Short Biography Carlos Trenado received his BSc and MSc degrees in mathematics from the National Autonomous

University of Mexico (UNAM) and New Mexico State University, respectively. He obtained a PhD

degree in neurotechnology at the faculty of medicine of Saarland University, Germany. Currently, he is

a postdoctoral fellow at the Cortical Motor Control Laboratory of the Freiburg University Hospital (Head

Prof. Dr. Dr. Rumyana Kristeva). His current research interests include improved performance via

stochastic resonance for rehabilitation applications and modeling.


E04P

Soft robotic hand orthosis for assistance and therapy in activities of daily living

J. Arata1*, R. Gassert

2, O. Lambercy

2, N. Mukae

1, M. Mori

1, M. Hashizume

1

1 Kyushu University, Japan 2 ETH Zurich, Switzerland

Abstract Robotics technology has recently been adapted for the rehabilitation of hand function following

neurological injury, in order to provide physical therapy, quantitative assessments of recovery, as well

as support for activities of daily living (ADL) at home. While various developments have been

proposed, it remains challenging to provide reasonably compact, lightweight, robust and affordable

robotic devices that can cope with the complexity and versatility of the human hand. Hand exoskeleton

devices typically involve a serially connected mechanical chain to transmit motion to the distal joints,

and are thus inherently bulky, heavy and complex. To tackle this problem, we developed a soft finger

mechanism that consists of three parallel spring blades at each joint. These allow aligning the remote

rotation center of the mechanism with that of the finger joint, while providing a compact and lightweight

implementation. The developed exoskeleton prototype consists of five finger modules that are

actuated by a linear motor. The device is capable of assisting grasping motion in both flexion and

extension, with a maximum of 10 N flexion/extension force output. The prototype is controlled by an

EMG sensor attached to the user’s forearm. The total mass of the hand exoskeleton is 202 g including

the linear motor. A single finger module is 4.7 mm in thickness and 5 mm in width, for a mass of only 7

g. The control unit can be attached on the user’s waist (200 g) including a battery that lasts

approximately one day. The key novelty of the developed device is that the spring mechanism is

inherently compliant, allowing for a natural adaptation of the finger position during the grasping of

complexly-shaped objects or exploration. In addition, the compliance is beneficial for the safety of

user. The device is currently being clinically evaluated with stroke patients.

References [1] Arata, J., Ohmoto, K., Gassert, R., Lambercy, O., Fujimoto, H., Wada, I., (2013) A new hand exoskeleton

device for rehabilitation using a three-layered sliding spring mechanism. Proc. of Int. Conf. on Robotics and Automation (ICRA), 3887-3892.

Short Biography Jumpei Arata received a PhD in mechanical engineering from the University of Tokyo, Japan, in 2004.

During 1998 and 2001, he spent two years at the Swiss Federal Institute of Technology, Lausanne,

Switzerland, on a grant from an international exchange program and a Swiss Federal Scholarship. He

is currently an associate professor at the Center for Advanced Medical Innovation, Kyushu University,

Japan. His current research interests include flexible mechanisms, haptic devices, and medical robots.


E05P

Rhythmic auditory stimulation for robot-assisted hand function training in stroke therapy

F. Speth1*, M. Wahl

1

1 Humboldt University Berlin, Institute of Rehabilitation Science

Abstract Robot-assisted hand-function-training (RT) complements conventional treatment effectively

[1]. “Rhythmic auditory stimulation” (RAS), an effective therapeutic technique for gait- and arm-training

in post-stroke-treatment [2], was never applied nor evaluated for RT. Four specified RAS-designs for

RT are suggested (metronome, spearcon-beat [3], waltz-music, multisensorical-beat). Four pilot-

experiments (E1-4) investigated effects of RAS-designs on function and motivation. In E1-3 effects

during performance of the Nine-Hole-Peg-Test were evaluated with healthy-subjects (n=20), in E4

during the Box-and-Block-Test with stroke-patients (n=9). Results showed, function and motivation

were better with rhythmic-stimulation than without, and motivation was best with waltz-music.

Strongest effects on function were seen with metronome in E2,3, in E1, 4 with waltz-music. As results

indicate RAS improves function and motivation, waltz-music, and/or metronome are proposed for

observations in RT.

References [1] Kwakkel G., Kollen B.J., and Krebs H.I.(2008). Effects of Robot-Assisted Therapy on Upper Limb Recovery

After Stroke: A Systematic Review. Neurorehabil Neural Repair, 22(2), 111–121.

[2] Altenmüller E.,and Schlaug G. (2013). Neurologic music therapy: The beneficial effects of music making in neurohabilitation. Acoust. Sc. & Tech., 34(1), 5–12.

[3] Dingler T., Lindsay J., and Walker B.N. (2008). Learnability of sound cues for environmental features: auditory icons, earcons, spearcons, and speech. ICAD , 2008: 1–6

Short Biography In her PhD, Florina Speth researches on effects of sound in robot-assisted hand function training for

stroke patients. In 2011 she gained her M.A. in Cognitive Musicology, Neurolinguistics and Music

Therapy at the University Cologne. In her Magister thesis she developed a multimodal stimulating

prototype for post-stroke hand rehabilitation-training. 2010-2012 she was working as research-

assistant at a neurological rehabilitation clinic within a study for the development of a test-battery

grading spasticity in paretic upper limbs of stroke patients. At the same time she was working as

neurological music therapist. 1995-2001 she studied Violoncello at the University Mozarteum Salzburg

as young-student.


E07P

Adaptive, robot assisted training for wrist motion recovery in a young sub-acute stroke subject

F. Marini1*, V. Squeri

1, L. Doglio

2, P. Moretti

2, P. Morasso

1, L. Masia

3

1 Robotics, Brain and Cognitive Sciences Dept, Istituto Italiano di Tecnologia, Genoa, Italy

2 Physical Medicine and Rehabilitation, Institute “G. Gaslini” Genova, Italy

3 School of MAE, Nanyang Technological, University of Singapore

Abstract Several studies support the hypothesis that robot assisted therapy for adult stroke subjects can be

beneficial in promoting the recovery process when conveyed in conjunction to conventional

rehabilitation. Robotic therapy has been mainly applied to adults subjects and, little is known about

children or youth. This study presents preliminary results of a robot-assisted rehabilitation training

protocol applied to a 14 years old subject suffering from sub-acute stroke. Robotic therapy was

applied to the distal portion of her left, affected upper limb in order to reduce the degree of motor

impairment. The left wrist of the subject was attached to a fully backdrivable exoskeleton that allows

full range of motion over the three degrees of freedom of the joint. The training protocol consisted of 3

months of therapy sessions. The subject was asked to perform goal-directed, planar reaching

movements, aiming at a target located at the limit of the workspace. To complete the task, the patient

had to move the end-effector of the robot while playing a simple but engaging video-game. Target

position’s and the robot’s configuration were chosen to constrain the movement to flexion/extension

and radial/ulnar deviation over the subject's entire supported active range of motion (ROM), evaluated

at the beginning of training sessions by measuring the amplitude of the voluntary movements in the

two aforementioned directions. The robot provided a variable assistance to complete the movements,

modulated according to the patient’s motor abilities detected by monitoring when the speed of the

robot end-effector exceeds a certain threshold. In order to assess subject’s improvements we

measured the time the subject took to complete the task, the amount of the assistance needed from

the robot, the accuracy of the matching position, and the smoothness of the trajectories. The results

show that robot-assisted training of reaching movements successfully improved arm movement ability

and promoted upper extremity functional recovery. Moreover they highlight the effectiveness of robotic

therapy for both dynamic and kinematic changes at the beginning and end of the experiment.

References [1] V. Squeri, L. Masia, P. Giannoni, G. Sandini, and P. Morasso, "Wrist Rehabilitation in Chronic Stroke Patients

by Means of Adaptive, Progressive Robot-Aided Therapy," Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 22, pp. 312-325, 2014.

[2] Kahn, L. E., Lum, P. S., Rymer, W. Z., & Reinkensmeyer, D. J. (2006). Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? The Journal of Rehabilitation Research and Development, 43(5), 619. doi:10.1682/JRRD.2005.03.0056

Short Biography Francesca Marini graduated in Biomedical Engineering at the University of Rome "La Sapienza" in

2013, with a master thesis in biomechanics concerning the validation of an innovative procedure for

sensor to segment calibration in gait analysis. She did an internship at the mechanical and thermal

measurements lab (Mechanical and Aerospace engineering division) of "La Sapienza" and at the

Movement Analysis and Robotics LABoratory (MARLAB) at the Children's hospital Bambino Gesù, of

Palidoro (Rome). In April 2013 she was recruited as research fellow at the Italian Institute of

Technology (IIT). Since January 2014, under the supervision of Prof. Pietro Morasso and Prof.

Lorenzo Masia, she has been following a PhD program in Cognitive Robotics, Interaction and

Rehabilitation Technologies at IIT. Her PhD project is mainly focused on robotic rehabilitation in

children with neurological injuries.


E08P

Upper limb measurement tools for children with neuromotor disorders: A systematic review

C. N. Gerber1,2,3

*, R. Labruyère1,2

, H. van Hedel1,2

1 PRRG, Rehabilitation Center for Children and Adolescents, Affoltern am Albis, Switzerland

2 Children’s Research Center, University Children’s Hospital Zurich, Switzerland

3 Department of Health Sciences and Technology, ETH Zurich, Switzerland

Abstract To investigate the effectiveness of upper limb rehabilitation, reliable and responsive measures of

upper limb function, capacity and performance are needed. Lately, some reviews have addressed

psychometrics of upper limb outcome measures for children. Yet, most of them focused on a specific

patient and/or age group and it remains unclear whether results can be generalized to children with

broader diagnoses or age ranges, as seen normally in pediatric neurorehabilitation settings.

This systematic review aimed to depict the evidence concerning reliability and responsiveness of

upper limb measurement tools used in pediatric neurorehabilitation.

A two-tiered search was conducted between December 2012 and June 2013. The first search

identified upper limb outcome measures for 1-18 years old children with neuromotor disorders. The

second examined the psychometric properties of the tools included. Two independent reviewers rated

the methodological quality of the included papers with the COSMIN checklist1. A „best evidence

synthesis“2 was performed to assemble information about each measurement tool.

The first search delivered 1566 hits. Of these, 84 papers were included. The screening of the retrieved

papers revealed 39 upper limb assessment tools. In 51 papers, data about reliability was reported.

Responsiveness was outlined in 7 studies whereas 10 studies provided information about the

measurement error. In total, 17 studies were of poor, 32 of fair, and 6 of good or excellent

methodological quality.

Very few tools with at least a moderate positive level of evidence are available for children with

cerebral palsy and very few psychometric studies involved children with other diagnoses than cerebral

palsy. To date no study of at least fair methodological quality about responsiveness of upper extremity

outcome tools for children with neuromotor disorders exists.

References [1] Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., Bouter L. M., de Vet, H.

C. W. (2010). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Quality of Life Research, 19(4), 539–49

Short Biography Corinna Gerber studied Human Movement Sciences at the ETH in Zurich. Since 2012 she is a PhD

student in Health Sciences and Technology and works in the Pediatric Rehab Research Group

(PRRG) in Affoltern am Albis, Switzerland.


E10P

MyHand: Assistive hand orthosis to overcome stroke impairments

J. van Wijngaarden1, L. Smulders

1*, G.-J. Lijbers

1, G. B. Prange

2, P. Veltink

1, A. Stienen

1

1 Biomechanical Engineering, University of Twente, Enschede, NL

2 Roessingh Research and Development, Enschede, NL

Abstract Many stroke patients suffer from impaired motor control, synergies and overactive flexor muscles in

the arm and hand [1]. The peak aperture of the hand is often greatly reduced, preventing the patient

from effectively grasping and manipulating objects and hindering them during Activities of Daily Life

(ADL). The MyHand project aims to improve the functional hand use of stroke patients during ADL at

home. For this goal, we are developing an active hand orthosis that can be worn throughout the day

and will assist hand movements through active compensation of the neural impairments. This "therapy

at home" should help to prevent contractures and learned disuse.

After stroke, the affected hand tends to become the supporting hand and the non-affected hand the

dominant hand during bimanual activities. The MyHand orthosis will help achieve cylindrical and

lateral grasps, which are the most frequently used supporting hand movements. To achieve this, an

advanced detection system is needed that will be able to make a distinction between desired

movements and undesired muscle activation caused by abnormal muscle control (spasticity,

synergies, etc.). The orthosis then needs to compensate for the undesired muscle activation, while

allowing the user to perform voluntary movements. Furthermore, the orthosis needs to be wearable all

day long. This puts high demands on the mobility, wearability, usability, aesthetics and safety of the

device. Key design aspects in user acceptance have been determined through user interviews. These

are easy donning and doffing, high comfort, low weight, compact size and easy cleaning of the

orthosis.

In the current concept, only extension of the fingers is actively controlled through a single

actuator with flexible connections to each finger. Thumb abduction will be actuated too, but the wrist is

passively supported using an elastic palmar cuff. Muscle activity is measured of the extensor and

flexor muscles in the forearm and the deltoids of the shoulder.

References [1] Raghavan, P. (2007), The nature of hand motor impairment after stroke and its treatment, Current Treatment

Options Cardiovascular Medicine; 9:221–228

Short Biography Johannes van Wijngaarden has a BSc in Technical Medicine and is working towards his MSc in

Biomedical Technology. Laura Smulders has a BSc in Industrial Design Engineering and is working

towards a double MSc in Industrial Design Engineering and Biomedical Technology. Both are in the

MSc Design Honors program at the University of Twente. Arno Stienen is an Assistant Professor at

the University of Twente and specializes in upper extremity motor learning and rehabilitation. MyHand

is supported by Fonds NutsOhra (NL, #558695).


E11P

Disorganization of motor unit control in paretic hand muscle post-stroke

N. L. Suresh1*

, X. Hu1, W. Z. Rymer

2

1 Rehabilitation Institute of Chicago

2 Department of Biomedical Engineering, Northwestern University

Abstract The potential mechanisms underlying paresis for voluntary motion following a cerebral stroke are

varied in nature.

Possibilities include reduced central drive, disuse atrophy, motoneuron loss, reinnervation subsequent

to MN loss, muscle contracture and inefficient activation of muscle. To date, studies directed at

understanding the role of inefficient motor unit(MU) activation have been conducted primarily using

intramuscular recordings, which are often time consuming with low unitary yields. Recently, a novel

EMG system(dEMG) from Delsys Inc., provides the user with automated MU decomposition of the

surface EMG signal recorded from a surface sensor array electrode. In addition to information

regarding MU recruitment threshold and mean firing rates(MFR), the surface recordings provide

information regarding individual motor unit action potential(MUAP) structural parameters, such as

MUAP amplitude and duration.

The objective of this study was to examine the possible contribution of disordered control of motor unit

(MU) recruitment and firing patterns to muscle weakness post-stroke. The dEMG system was used to

record sEMG signals and extract MU parameters from the first dorsal interosseous muscle (FDI) of

both sides of three hemiparetic stroke survivors. An estimate of the MUAP amplitude was derived

using spike-triggered averaging of the sEMG signal. The relationship between p-p MUAP amplitude

and MU threshold (reflecting the size principle) as well as between MU MFR and MU threshold

(reflecting onion skinning) was compared between the two sides of each stroke subject. Our

preliminary results suggest a disrupted orderly recruitment based on MUAP size, a compressed

recruitment range, and a lack of the ‘onion skin’ pattern due to saturated MU firing rates on the paretic

side. In contrast, MU organization was similar bilaterally for the subject with minor impairment. These

results suggest that MU organizational changes with respect to recruitment and rate modulation can

contribute to muscle weakness post-stroke.

References [1] Hu X, Rymer WZ, Suresh NL (2013) Motor unit pool organization examined via spike triggered averaging of

the surface electromyogram. Journal of Neurophysiology, 110(5): 1205-20.

[2] Gemperline JJ, Allen S, Walk D, and Rymer WZ (1995) Characteristics of motor unit discharge in subjects with hemiparesis. Muscle Nerve, 18(10): 1101-14.

Short Biography Nina Suresh has a Bachelor of Science degree in Computer Science and Mathematics and a Ph.D.

degree in Biomedical Engineering from the University of Illinois, Chicago. She is currently a Research

Scientist with the Rehabilitation Institute of Chicago(RIC). Nina’s overall research interests include

understanding the neural mechanisms underlying paresis and spasticity in stroke survivors as well as

the characterization of task dependent differences in muscle activation in multifunctional muscle.


E12P

Initial clinical trial of the Exo-Glove, a soft wearable robot for the hand: the case of tetraplegia (C4)

H. In1, U. Jeong

1, B. B. Kang

1, B. Kim

1, K.-J. Cho

1*

1 School of Mechanical and Aerospace Engineering/Seoul National University Institute of Advanced Machinery

and Design (SNU-IAMD), Seoul National University

Abstract We will present the Exo-Glove, a tendon-driven soft wearable robot for the hand, and its initial clinical

trial to a person with tetraplegia. The Exo-Glove is driven by the tendons attached to the glove with a

special design and fabrication methods, which enable the wearable robot to be lightweight and

compact, but allow the person with hand disability to grasp various different shaped objects with

simple control. Grasping of various-shaped objects with the Exo-Glove was attempted by a healthy

subject and a subject with SCI(C4). The subject had been injured 6 months ago, and cannot flex and

extend the fingers and the thumb at all but can move the elbow and the shoulder. Fig. 1 shows the

grasping by the disabled subject using Exo-Glove. Both subjects grasped all the target objects.

Because of the soft structure, the Exo-Glove has different properties compared with rigid wearable

robots. It’s not just lightweight and compact, but the unconstrained d.o.f. of the Exo-Glove and the

different finger properties could generate different trajectory from person to person depending on the

finger properties. More tendons can be attached to generate pull forces on specific points.

Understanding how these characteristics could affect the person with disability neurologically would be

valuable to further develop the Exo-Glove to be used for rehabilitation.

References [1] In, H., Cho, K.-J., Kim, K. and Lee, B. (2011). Jointless structure and under-actuation mechanism for compact

hand exoskeleton. 2011 IEEE International Conference on Rehabilitation Robotics (ICORR). 1–6.

Short Biography Kyu-Jin Cho received the B.S and M.S. degrees from Seoul National University, Seoul, Korea, in 1998

and 2000, respectively, and the Ph.D. degree in mechanical engineering from the Massachusetts

Institute of Technology, Cambridge, in 2007. He was a Postdoctoral Fellow at the Harvard

Microrobotics Laboratory until 2008. He is currently an Associate Professor of Mechanical and

Aerospace Engineering and the Director of the Biorobotics Laboratory, at SNU, Seoul, Korea. His

research interests include soft biologically inspired robotics, and rehabilitation/ assistive robotics. He

has received the PaikAm Award from Korean Society of Precision Engineering in 2013 and the Early

Career Award from IEEE Robotics and Automation Socoety in 2014.

Fig. 1. Various grasping motions with Exo-Glove performed by subject with paralysis in the hand.


E13P

The use of ecological sounds in facilitation of tool use in apraxia

M. Bieńkiewicz1*, P. Gulde

1, J. Hermsdörfer

1

1 Technical University of Munich, Sport and Health Department

Abstract The CogWatch project aims to create an intelligent assistance system to improve activities of daily

living (ADL) in stroke survivors, who suffer from impaired ability to use everyday tools (apraxia). The

core of this symptom lies in the compromised ability to access the appropriate motor program relevant

to the task goal. Patients demonstrate difficulties during the execution of single and multiple tool use

and during the pantomime of the same movement [1]. There is lack of evidence whether sensory

cueing can facilitate tool use in those patients if presented prospectively to a patient. This study

explores the use of cues, based on ecological sound linked to the action goal, in the facilitation of

pantomime and actual tool use. Eco-acoustics define environmental sound as an audible product of

physical event, caused by interaction of the materials. Recent research suggests that motor networks

associated with mirror neurons respond to the action-related sounds [2]. In this study, three ADL tasks

were introduced: hammering, sawing, and tooth brushing. Ten patients with left brain damage and five

patients with right brain damage following first cerebrovascular accident were tested. In addition,

twenty age-matched controls were tested, in the same experimental paradigm, ten in the non-

dominant hand performance. The study comprised of four different cueing modes (prior to task

execution): no cues, auditory instruction, pictorial instruction, and ecological sounds. Comparison of

performance across these conditions incorporated a video-based error assessment, overall

performance scoring, along with the analysis of kinematic outcome measures and movement

variability. Environmental sound display prior to task execution produced multi-dimensional facilitation

of the movement in selected patients in both tool use and pantomime mode. The findings from this

study will support the development of Cogwatch system that aims to provide automatized guidance for

the patients during home- based ADL after dismissal from the hospital.

References [1] Goldenberg, G. (2013). Apraxia: The Cognitive Side of Motor Control. OUP Oxford

[2] Ticini, L., Schutz-Bosbach, S., Weiss, C., Casile, A., & Waszak, F. (2013).When sounds become actions: higher-order representation of newly learned action sounds in the human motor system. Journal of Cognitive Neuroscience, 24(2), 464-474.

Short Biography My principal research interest lies in the field of motor control and neuropsychology of perception and

action. I have joined Department of Sport and Health Science, TUM in July 2012 to work as a Post-

doctoral Fellow in the Cogwatch project www.cogwatch.eu. My current line of work is dedicated to

understanding how external sensory information can guide performance in clinical populations with

motor and cognitive disorders.

http://www.cogwatch.eu/


E14P

Neurocognitive robot-assisted rehabilitation of hand function

O. Lambercy1*, J.-C. Metzger

1, A. Califfi

2, D. Dinacci

2, C. Petrillo

2, P. Rossi

2, F. M. Conti

2, R. Gassert

1

1 Rehabilitation Engineering Lab, ETH Zurich, Switzerland

2 Clinica Hildebrand Centro di riabilitazione Brissago, Switzerland

Abstract Over the past decades, robotic devices have been tested for their beneficial effect on the recovery

process following stroke. The present work investigated unexploited potential in upper limb robot-

assisted rehabilitation by adopting a therapy concept focused on hand function and incorporating the

training of haptic perception. Neurocognitive therapy – a concept requiring patients to solve cognitive

problems based on expected and perceived somatosensation – was implemented on the

ReHapticKnob, a rehabilitation robot to train hand function. Several exercises focusing on the

interaction with virtual objects rendered by the robot (e.g. grasping a virtual sponge) and the

identification of haptic stimuli (e.g. amplitude of passive finger displacement) were implemented on the

robot and evaluated in a clinical study.

Eighteen subacute stroke patients participated in a four-week randomized controlled trial. The

intervention group (N=8) received robotic therapy during four 45-minute sessions per week over four

weeks. A dose-matched control group (N=10) received conventional neurocognitive therapy without

the robot. In parallel, both groups attended their daily rehabilitation program at the clinic. Motor,

sensory, cognitive and robotic assessments were conducted before the start and after the completion

of the robot-assisted therapy, as well as in a follow-up session.

Patients from both groups showed reduced motor impairment (Fugl-Meyer Assessment of the Upper

Extremity (FMA-UE)) and significant functional improvements in dexterity (Box and Block Test). A non-

significant trend of improved sensory perception was also observed in both groups. Neurocognitive

robot-assisted rehabilitation was shown to be clinically well accepted, and equally beneficial as

conventional neurocognitive therapy in terms of upper limb motor impairment reduction. The

ReHapticKnob further has the potential to objectively assess and monitor patients throughout therapy

progression using accurate and reliable sensor measurements.

References [1] Metzger, J.-C., Lambercy, O., Chapuis, D., and Gassert, R. (2011). Design and characterization of the

ReHapticKnob, a robot for assessment and therapy of hand function. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 3074-3080.

[2] Metzger, J.-C., Lambercy, O., Califfi, A., Conti, F. and Gassert, R. (2014). Neurocognitive Robot-Assisted Therapy of Hand Function. IEEE Transactions on Haptics, 7(2):140-149.

Short Biography Olivier Lambercy received the MSc degree in microengineering from the Ecole Polytechnique

Fédérale de Lausanne, Lausanne, Switzerland, in 2005, and the PhD degree in mechanical

engineering from the National University of Singapore, Singapore in 2009. He is currently Senior

Research Associate at the Rehabilitation Engineering Lab at ETH Zurich, Switzerland. His main

contributions are in the field of robot-assisted rehabilitation of hand function after stroke, and his

research interests include medical and rehabilitation robotics, motor control and human-machine

interaction.


E15P

Robotic assessment of finger proprioception

M. D. Rinderknecht1*, W. L. Popp

1, O. Lambercy

1, R. Gassert

1

1 Rehabilitation Engineering Lab, ETH Zurich, Zurich, Switzerland

Abstract Sensory function, and proprioception in particular, plays a crucial role in the learning and execution of

motor actions. Neurological injuries, such as stroke, can lead to severe sensory deficits affecting

quality of life, especially when they occur at the level of the hand. Moreover, reduced sensory function

has been linked to poor prognosis of functional recovery after stroke. Nevertheless, sensory deficits

are rarely addressed in current clinical settings and clinicians lack proper assessment tools for the

assessment of proprioceptive deficits.

We propose a robotic assessment tool for the qualitative, automated evaluation of proprioception in

the hand by measuring the joint angle position difference threshold (DL) in the metacarpophalangeal

joint of the index finger. The experimental evaluation of an adaptive algorithm called Parameter

Estimation by Sequential Testing (PEST) is presented by comparing it with the frequently used but

limited method of constant stimuli (MOCS), both in combination with a two-interval two-alternative

forced choice (2AFC) paradigm. The results of a pilot study with 13 healthy young subjects showed

DLs in a similar range for PEST (1.73°±0.78°) and MOCS (2.15°±0.77°). However, no significant

correlation between the two methods was found. Nevertheless, the number of trials could be reduced

by approximately 50% using PEST, resulting in an assessment time of less than 15 minutes.

The test-retest reliability of this new assessment method using PEST and 2AFC is currently being

evaluated in a study at the Kliniken Schmieder Allensbach, Germany, with stroke patients and age-

matched healthy elderly subjects. Robotic assessment outcomes will be correlated with clinical scales

in order to assess the validity of the proposed method. We expect that this new assessment approach

not only has the potential to allow clinicians to monitor recovery, but might also help in designing

adequate therapies that target the recovery of sensory function.

References [1] Rinderknecht MD, Popp W, Lambercy O, Gassert R. (2014). Experimental Validation of a Rapid, Adaptive

Robotic Assessment of the MCP Joint Angle Difference Threshold. Eurohaptics.

Short Biography Mike Domenik Rinderknecht began his Ph.D. studies on robotic sensory assessment and therapy of

hand function after stroke at the Rehabilitation Engineering Lab (RELab) at ETH Zurich in June 2013.

He received his B.Sc. and M.Sc. in Microengineering in 2010 and 2012, respectively, both from Ecole

Polytechnique Fédérale de Lausanne (EPFL), with a focus on robotics and autonomous systems. He

further obtained a minor in Biomedical Technologies. As a visiting research student at the

Collaborative Haptics and Robotics in Medicine Laboratory (CHARM Lab) at Stanford University he

investigated learning and transfer in isometric and dynamic reaching for stroke rehabilitation. His

research interests range from rehabilitation and medical robotics, assistive devices and prosthetics,

virtual reality and haptics to sensory-motor learning and neuroscience.


INDEX Section Page

PARTICIPANTS 108

AUTHORS 110


PARTICIPANTS Last Name, First Name Email ID Page Abdi, Elahe [email protected] ........................................................... B02P 74 Akselrod, Michel [email protected] .................................................. B13P 80 Arata, Jumpei [email protected] .................................... E04P 97 Beckers, Niek [email protected] ........................................................... Bensmaia, Sliman [email protected] ............................................. KEYNOTE 39 Bienkiewicz, Marta [email protected] .............................................. E13P 104 Bleuler, Hannes [email protected]......................................... B02P, C03P 74, 85 Brochier, Thomas [email protected] ................................ A13T, A14P 15, 71 Brugger, Peter [email protected] ............................................................... Bullock, Ian [email protected] ................................. D09P, D11T, D12P 93, 46, 94 Cho, Kyu-Jin [email protected] ............................................................... E12P 103 Conti, Fabio Mario [email protected] ........................................... E14P 105 Davare, Marco [email protected] .......................................................... A05T 20 de Bruin, Marije [email protected] .................................................. B09P, D06T 77, 51 Diedrichsen, Joern [email protected] ................................ KEYNOTE, A03P 21, 64 Dimitriou, Michael [email protected] ......................................................... D04T 45 Doix, Aude-Clémence [email protected] .......................................... D03P 90 Dollar, Aaron [email protected] .................... D09P, D10T, D11T, D12P 93, 49, 46, 94 Drozdowska, Bogna [email protected] ........................................................ B10P 78 Dueñas, Julio [email protected] .................................................................. A09P 68 Duff, Margaret [email protected] ................................................................ E03T 56 Eggenberger, Noëmi [email protected] ......................... B04T, B05P 28, 75 Ejaz, Naveed [email protected] ......................................... A03P, (KEYNOTE) 64, 21 Evans, Rachel [email protected] ....................................................... B11P 79 Faisal, Aldo [email protected] ......................... C02P, D07T, D08P 84, 52, 92 Feix, Thomas [email protected] ................................ D09P, D10T, D11T 93, 49, 46 Gassert, Roger [email protected] ....... A09P, B01T, B13P, E04P, E14P, E15P 68, 29, 80, 97, 105, 106 Gerber, Corinna [email protected] ............................................ E08P 100 Gindrat, Anne-Dominique [email protected] .......................... A06P, A10T 66, 22 Goldenberg, Georg [email protected] .......... KEYNOTE 26 Goldin-Meadow, Susan [email protected] .................................................. KEYNOTE 25 Gomez, Alice [email protected] ................................................... B08P 76 Gurd, Jennifer [email protected] ............................................ B07T 36 Hayward, Vincent [email protected] .................................. KEYNOTE 50 Hepp-Reymond, Marie-Claude [email protected] ............................... A09P, D01P, E01P 68, 88, 96 Hermsdörfer, Joachim [email protected] ................... KEYNOTE, E13P 54, 104 In, Hyunki [email protected] ......................................................... E12P 103 Intveld, Rijk [email protected] ................................................................ A07T 18 Ionta, Silvio [email protected]...................................................... B01T 29 Jaspers, Ellen [email protected] .............................................. D02P 89 Johansson, Roland [email protected] ....................... KEYNOTE 44 Kaeser, Melanie [email protected] ............................ A02P, A04P, A06P 63, 65, 66 Kamper, Derek [email protected] ........................................................ KEYNOTE 57 Kikkert, Sanne [email protected] .......................................... B14P 81 Klamroth-Marganska, Verena [email protected] ................................................. Kraskov, Alexander [email protected] ........................................................ A12P 70 Kristeva, Rumyana [email protected] ................ D01P, E01P 88, 96 Kuiken, Todd [email protected] ...................................... KEYNOTE 42 Lambercy, Olivier [email protected] ..................................... E04P, E14P, E15P 97, 105, 106 Lemon, Roger [email protected] ......................................... KEYNOTE, A12P 14, 70 Luppino, Giuseppe [email protected] ....................................................... KEYNOTE 16 Makin, Tamar [email protected] ......................... KEYNOTE, B14P 33, 81 Manjarrez, Elias [email protected] ................................... D01P, E01P 88, 96 Marchesotti, Silvia [email protected] ............................................... C03P 85 Marini, Francesca [email protected] ...................................................... E07P 99 Melendez-Calderon, Alejandro [email protected] ................................... E06T 58 Miller, Lee [email protected] .............................................. KEYNOTE 40 Milner, Ted [email protected] .......................................... E02T 55 Murray, Wendy [email protected] ................................ B09P, D06T 77, 51 Nizamis, Konstantinos [email protected] .................................................. Preisig, Basil [email protected] ..................................... B04T, B05P 28, 75 Qi, Huixin [email protected] .................................................. A11P 69 Radman, Zdravko [email protected] ................................................................. B06T 30 Riehle, Alexa [email protected] ....................................... A13T, A14P 15, 71 Rinderknecht, Mike Domenik [email protected] ...................................... E15P 106 Rouiller, Eric [email protected] .... KEYNOTE, A02P, A04P, A06P, A10T 47, 63, 65, 66, 22 Ruddy, Kathy [email protected] ................................................ D05P 91 Rusconi, Elena [email protected] ................................................. B12T 34


Rymer, William Zev [email protected] ........................... E03T, E11P 56, 102 Samur, Evren [email protected] ........................................................ Santello, Marco [email protected] ......................................... KEYNOTE 19 Sarma, Devapratim [email protected] ........................................ C04P 86 Savidan, Julie [email protected] ................................. A02P, A04P, A06P 63, 65, 66 Schaffelhofer, Stefan [email protected] .................................................... A16P 72 Scherberger, Hans [email protected] ............................. KEYNOTE, A07T, A16P 41, 18, 72 Schieber, Marc [email protected] .......................................... KEYNOTE 17 Schmidlin, Éric [email protected] ............................... A02P, A04P, A06P 63, 65, 66 Schrafl-Altermatt, Miriam [email protected] .............................................................. A15T 23 Schuler, Florian [email protected] .......................................................... A08P 67 Schwartz, Andrew [email protected] ........................................................ KEYNOTE 38 Serino, Andrea [email protected] ............................................. KEYNOTE 32 Sirigu, Angela [email protected] ...................................................... KEYNOTE 31 Smulders, Laura [email protected] ..................................... E10P 101 Speth, Florina [email protected] ......................................... E05P 98 Stienen, Arno [email protected] ........................................ E09T, E10P 59, 101 Suresh, Nina [email protected] ............................................ E11P 102 Thomik, Andreas [email protected] ............. C02P, D07T, D08P 84, 52, 92 Trenado Colin, Carlos Alberto [email protected] .................................. D01P, E01P 88, 96 Valero-Cuevas, Francisco [email protected] ....................................................... KEYNOTE 48 van Polanen, Vonne [email protected] .............................................................. Vingerhoets, Guy [email protected] .............................................. A01P 62 Vogt, Stefan [email protected] ......................................... B03T, C01P 35, 83 Wenderoth, Nici [email protected] .........KEYNOTE, D02P, D05P 27, 89, 91 Wilson, Frank [email protected] ........................................ KEYNOTE 5 Wohlman, Sarah [email protected] ................... B09P, D06T 77, 51


AUTHORS Author ID Page Abdi, E. ............................................................................................................................... B02P 74 Adriani, M. ............................................................................................................................ B12T 34 Akselrod, M. ........................................................................................................................ B13P 80 Amtage, F. .......................................................................................................................... E01P 96 Arata, J. ............................................................................................................................... E04P 97 Arnold, A. ............................................................................................................................ B10P 78 Bensmaia, S. J. .......................................................................................................... KEYNOTE 39 Bergsma, A. ......................................................................................................................... E09T 59 Bickerton, W.-L. .................................................................................................................. B11P 79 Bieńkiewicz, M. ................................................................................................................... E13P 104 Blanke, O. ................................................................................................................ B13P, C03P 80, 85 Blefari, M. L. ........................................................................................................................ C03P 85 Bleuler, H. ................................................................................................................ B02P, C03P 74, 85 Bouri, M. .............................................................................................................................. B02P 74 Brill, N. ......................................................................................................................(KEYNOTE) 40 Brochier, T. ............................................................................................................... A13T, A14P 15, 71 Bullock, I. M. ................................................................................................. D09P, D11T, D12P 93, 46, 94 Burdet, E. ............................................................................................................................ B02P 74 Califfi, A. .............................................................................................................................. E14P 105 Carroll, T. J. ........................................................................................................................ D05P 91 Carson, R. G. ...................................................................................................................... D05P 91 Cho, K.-J. ............................................................................................................................ E12P 103 Colangiulo, R. ..................................................................................................................... A02P 63 Contestabile, A. ................................................................................................................... A02P 63 Conti, F. M. ......................................................................................................................... E14P 105 Curt, A. ................................................................................................................................. B01T 29 Davare, M. ........................................................................................................................... A05T 20 de Bruin, M. .............................................................................................................. B09P, D06T 77, 51 Dehaene, G. ........................................................................................................................ B08P 76 Demarchi, G. ........................................................................................................................ B12T 34 Desloovere, K. .................................................................................................................... D02P 89 Di Dio, C. .............................................................................................................................. B03T 35 Diedrichsen, J. ............................................................................................... KEYNOTE, A03P 21, 64 Dietz, V. ............................................................................................................................... A15T 23 Dimitriou, M. ........................................................................................................................ D04T 45 Dinacci, D. ........................................................................................................................... E14P 105 Doglio, L. ............................................................................................................................. E07P 99 Doix, A.-C. M. ...................................................................................................................... D03P 90 Dollar, A. M. ....................................................................................... D09P, D10T, D11T, D12P 93, 49, 46, 94 Drozdowska, B. ................................................................................................................... B10P 78 Dueñas, J. ........................................................................................................................... A09P 68 Duff, M. ................................................................................................................................ E03T 56 Dumanian, G. ............................................................................................................(KEYNOTE) 42 Duschau-Wicke, A. .............................................................................................................. E06T 58 Edin, B. B. ........................................................................................................................... D03P 90 Eggenberger, N. ........................................................................................................ B04T, B05P 28, 75 Ejaz, N. .......................................................................................................... A03P, (KEYNOTE) 64, 21 Essig, F. ............................................................................................................................... B07T 36 Ethier, C ....................................................................................................................(KEYNOTE) 40 Evans, R. ............................................................................................................................ B11P 79 Faisal, A. A. ................................................................................................... C02P, D07T, D08P 84, 52, 92 Feix, T. .......................................................................................................... D09P, D10T, D11T 93, 49, 46 Fenske, S. ............................................................................................................................ D08P 92 Ferrari, P. ............................................................................................................................. B12T 34 Feys, H. ............................................................................................................................... D02P 89 Fortis, E. .............................................................................................................................. A06P 66 Franco, B. ..................................................................................................................(KEYNOTE) 40 Fregosi, M. ............................................................................................................... A04P, A06P 65, 66 Freund, P. ............................................................................................................................ B01T 29 Fringi, E. .............................................................................................................................. B10P 78 Furlan, M. ............................................................................................................................. B12T 34 Gassert, R. .................................................................... A09P, B01T, B13P, E04P, E14P, E15P 68, 29, 80, 97, 105, 106 Gerber, C. N. ....................................................................................................................... E08P 100 Gharbawie, O. A. ................................................................................................................ A11P 69 Ghosh, A. ............................................................................................................................. A10T 22 Gindrat, A.-D. ............................................................................................................ A06P, A10T 66, 22 Goldenberg, G. .......................................................................................................... KEYNOTE 26 Goldin-Meadow, S. .................................................................................................... KEYNOTE 25


Gomez, A. ........................................................................................................................... B08P 76 Grün, S. ............................................................................................................................... A14P 71 Gulde, P. ............................................................................................................................. E13P 104 Gurd, J. ................................................................................................................................ B07T 36 Haber, D. ............................................................................................................................. C02P 84 Haggard, P. .......................................................................................................................... B12T 34 Hamadjida, A. ..................................................................................................................... A02P 63 Hao, Y. ................................................................................................................................. A13T 15 Hargrove, L. ..............................................................................................................(KEYNOTE) 42 Hashizume, M. .................................................................................................................... E04P 97 Hayward, V. ................................................................................................................ KEYNOTE 50 Heinzle, J. ........................................................................................................................... A08P 67 Henderson-Slater, D. .......................................................................................................... B14P 81 Hepp-Reymond, M.-C. .................................................................................. A09P, D01P, E01P 68, 88, 96 Hermsdörfer, J. .............................................................................................. KEYNOTE, E13P 54, 104 Higuchi, S. ............................................................................................................................ B03T 35 Honoré, P. ........................................................................................................................... A01P 62 Hopfner, S. ........................................................................................................................... B04T 28 Hu, X. .................................................................................................................................. E11P 102 Hudson, J. ............................................................................................................................ E03T 56 Huethe, F. ................................................................................................................ D01P, E01P 88, 96 Huidobro, N. ........................................................................................................................ D01P 88 Humphreys, G. .................................................................................................................... B11P 79 Huron, C. ............................................................................................................................. B08P 76 In, H. .................................................................................................................................... E12P 103 Ingvast, J. ............................................................................................................................. E06T 58 Intveld, R. W. ....................................................................................................................... A07T 18 Ionta, S. ................................................................................................................................ B01T 29 Jaspers, E. .......................................................................................................................... D02P 89 Jeong, U. ............................................................................................................................. E12P 103 Jobert, A. ............................................................................................................................. B08P 76 Johansen-Berg, H. .............................................................................................................. B14P 81 Johansson, R. S. ....................................................................................................... KEYNOTE 44 Jutzeler, C. ........................................................................................................................... B01T 29 Kaas, J. H. .......................................................................................................................... A11P 69 Kaeser, M. ...................................................................................................... A02P, A04P, A06P 63, 65, 66 Kamper, D. ................................................................................................................. KEYNOTE 57 Kang, B. B. .......................................................................................................................... E12P 103 Kazemi, H. ........................................................................................................................... E02T 55 Kikkert, S. ............................................................................................................................ B14P 81 Kim, B. ................................................................................................................................ E12P 103 Kollias, S. ............................................................................................................................ A09P 68 Koopman, B. ........................................................................................................................ E09T 59 Kottink, A. I. R. ..................................................................................................................... E06T 58 Kraskov, A. .......................................................................................................................... A12P 70 Kristeva, R. .............................................................................................................. D01P, E01P 88, 96 Kuiken, T. ................................................................................................................... KEYNOTE 42 Kumar, V. ............................................................................................................................ C04P 86 Labruyère, R. ...................................................................................................................... E08P 100 Lambercy, O. ................................................................................................. E04P, E14P, E15P 97, 105, 106 Lau, J. K. ............................................................................................................................. B11P 79 Lemon, R. N. ................................................................................................... KEYNOTE, A12P 14, 70 Lijbers, G.-J. ........................................................................................................................ E10P 101 Linkenauger, S. ................................................................................................................... C01P 83 Luppino, G. ................................................................................................................ KEYNOTE 16 Ma, R. R. ............................................................................................................................. D12P 94 Makin, T. R. ..................................................................................................... KEYNOTE, B14P 33, 81 Manjarrez, E. ............................................................................................................ D01P, E01P 88, 96 Marchesotti, S. .................................................................................................................... C03P 85 Marini, F. ............................................................................................................................. E07P 99 Martin, K. A. C. .................................................................................................................... A08P 67 Martuzzi, R. .............................................................................................................. B13P, C03P 80, 85 Masia, L. ............................................................................................................................. E07P 99 Melendez-Calderon, A. ........................................................................................................ E06T 58 Mendez-Balbuena, I. ................................................................................................ D01P, E01P 88, 96 Metzger, J.-C. ..................................................................................................................... E14P 105 Mezue, M. ........................................................................................................................... B14P 81 Mikulić, A. ............................................................................................................................ D01P 88 Miller, L. ..................................................................................................................... KEYNOTE 40 Milner, T. .............................................................................................................................. E02T 55 Morasso, P. ......................................................................................................................... E07P 99 Moretti, P. ............................................................................................................................ E07P 99 Mori, M. ............................................................................................................................... E04P 97 Mouthon, M. ........................................................................................................................ A04P 65


Mukae, N. ............................................................................................................................ E04P 97 Müri, R. M. ................................................................................................................ B04T, B05P 28, 75 Murray, W. M. .......................................................................................................... B09P, D06T 77, 51 Naufel, S. ...................................................................................................................(KEYNOTE) 40 Nuruki, A. ............................................................................................................................. A05T 20 Nys, J. ................................................................................................................................. A01P 62 Ojeman, J. G. ...................................................................................................................... C04P 86 O’Shea, J. ........................................................................................................................... B14P 81 Petrillo, C. ........................................................................................................................... E14P 105 Philipp, R. ............................................................................................................................ A12P 70 Piazza, M. ........................................................................................................................... B08P 76 Plettenburg, D. ..................................................................................................................... E09T 59 Popp, W. L. ......................................................................................................................... E15P 106 Prange, G. B. ............................................................................................................ E06T, E10P 58, 101 Preisig, B. C. ............................................................................................................. B04T, B05P 28, 75 Pruszynski, J. A. .......................................................................................................(KEYNOTE) 44 Qi, H.-X. .............................................................................................................................. A11P 69 Radman, Z. .......................................................................................................................... B06T 30 Rao, R. P. N. ....................................................................................................................... C04P 86 Reed, J. L. ........................................................................................................................... A11P 69 Reichenbach, A. .................................................................................................................. A03P 64 Riehle, A. .................................................................................................................. A13T, A14P 15, 71 Rinderknecht, M. D. ............................................................................................................ E15P 106 Rizzolatti, G. ......................................................................................................................... B03T 35 Roberts, N. ........................................................................................................................... B03T 35 Rosch, R. ............................................................................................................................. B07T 36 Rossi, P. .............................................................................................................................. E14P 105 Rotshtein, P. ............................................................................................................ B10P, B11P 78, 79 Rouiller, E. M. ................................................................ KEYNOTE, A02P, A04P, A06P, A10T 47, 63, 65, 66, 22 Rouse, A. ..................................................................................................................(KEYNOTE) 17 Roux, C. ................................................................................................................... A04P, A06P 65, 66 Ruddy, K. L. ........................................................................................................................ D05P 91 Rusconi, E. ........................................................................................................................... B12T 34 Rymer, W. Z. ............................................................................................................. E03T, E11P 56, 102 Sakreida, K. ............................................................................................................. B03T, C01P 35, 83 Santello, M. ................................................................................................................ KEYNOTE 19 Sarma, D. ............................................................................................................................ C04P 86 Savidan, J. ..................................................................................................... A02P, A04P, A06P 63, 65, 66 Schaffelhofer, S. ................................................................................................................. A16P 72 Scherberger, H. ................................................................................... KEYNOTE, A07T, A16P 41, 18, 72 Schieber, M. H. .......................................................................................................... KEYNOTE 17 Schmidlin, E. .................................................................................................. A02P, A04P, A06P 63, 65, 66 Schrafl-Altermatt, M. ............................................................................................................ A15T 23 Schuler, F. A. F. .................................................................................................................. A08P 67 Schulte-Mönting, J. .................................................................................................. D01P, E01P 88, 96 Schurger, A. ........................................................................................................................ C03P 85 Schwartz, A. B. .......................................................................................................... KEYNOTE 38 Seifritz, E. ............................................................................................................................ A09P 68 Serino, A. ................................................................................................................... KEYNOTE 32 Sirigu, A. ..................................................................................................................... KEYNOTE 31 Smulders, L. ........................................................................................................................ E10P 101 Speth, F. ............................................................................................................................. E05P 98 Spierer, L. ........................................................................................................................... A04P 65 Squeri, V. ............................................................................................................................ E07P 99 Stämfli, P. ............................................................................................................................ A09P 68 Stienen, A. ................................................................................................................ E09T, E10P 59, 101 Sulzer, J. .................................................................................................................. A09P, B13P 68, 80 Suresh, N. L. ....................................................................................................................... E11P 102 Tamè, L. ............................................................................................................................... B12T 34 Thomik, A. ..................................................................................................... C02P, D07T, D08P 84, 52, 92 Tracey, I. ............................................................................................................................. B14P 81 Trenado, C. .............................................................................................................. D01P, E01P 88, 96 Turgeon, M. .......................................................................................................................... B03T 35 Tyler, D. ....................................................................................................................(KEYNOTE) 40 Valero-Cuevas, F. J. .................................................................................................. KEYNOTE 48 van Hedel, H. ...................................................................................................................... E08P 100 van Wijngaarden, J. ............................................................................................................ E10P 101 Vandekerckhove, E. ............................................................................................................ A01P 62 Vandemaele, P. .................................................................................................................. A01P 62 Veltink, P. ............................................................................................................................ E10P 101 Vigneswaran, G. ................................................................................................................. A12P 70 Vila, P. .................................................................................................................................. B07T 36 Villiger, M. ............................................................................................................................ B01T 29 Vingerhoets, G. ................................................................................................................... A01P 62


Vogt, S. .................................................................................................................... B03T, C01P 35, 83 Wahl, M. .............................................................................................................................. E05P 98 Waldert, S. .......................................................................................................................... A12P 70 Wander, J. D. ...................................................................................................................... C04P 86 Wenderoth, N. ......................................................................................KEYNOTE, D02P, D05P 27, 89, 91 Wilson, F. ................................................................................................................... KEYNOTE 5 Wing, A. .............................................................................................................................. B10P 78 Wohlman, S. J. ......................................................................................................... B09P, D06T 77, 51 Worthington, A. ........................................................................................................ B10P, B11P 78, 79 Wu, J. .................................................................................................................................. C04P 86 Wyss, A.-F. ......................................................................................................................... A02P 63 Zatka-Haas, P. .................................................................................................................... A03P 64 Ziessler, M. .......................................................................................................................... B03T 35 Zito, G. ................................................................................................................................ B05P 75

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